From 1f4f695c7ecbfacaaada2ddd490bd474ea98855a Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Mon, 10 Nov 2025 12:01:30 +0000 Subject: [PATCH 01/24] adding vs module Signed-off-by: Nathaniel --- causalpy/__init__.py | 1 + causalpy/experiments/instrumental_variable.py | 19 +- causalpy/pymc_models.py | 68 +- .../tests/test_integration_pymc_examples.py | 32 + causalpy/variable_selection_priors.py | 591 ++++++++++++++++++ docs/source/_static/interrogate_badge.svg | 6 +- 6 files changed, 698 insertions(+), 19 deletions(-) create mode 100644 causalpy/variable_selection_priors.py diff --git a/causalpy/__init__.py b/causalpy/__init__.py index 5587fb3e..09384669 100644 --- a/causalpy/__init__.py +++ b/causalpy/__init__.py @@ -41,4 +41,5 @@ "RegressionKink", "skl_models", "SyntheticControl", + "variable_selection_priors", ] diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py index 001ce9af..ebd64675 100644 --- a/causalpy/experiments/instrumental_variable.py +++ b/causalpy/experiments/instrumental_variable.py @@ -52,6 +52,12 @@ class InstrumentalVariable(BaseExperiment): "eta": 2, "lkj_sd": 2, } + :param vs_prior_type : str or None, default=None + Type of variable selection prior: 'spike_and_slab', 'horseshoe', or None. + If None, uses standard normal priors. + :param vs_hyperparams : dict, optional + Hyperparameters for variable selection priors. Only used if vs_prior_type + is not None. Example -------- @@ -99,6 +105,8 @@ def __init__( formula: str, model=None, priors=None, + vs_prior_type=None, + vs_hyperparams=None, **kwargs, ): super().__init__(model=model) @@ -108,6 +116,8 @@ def __init__( self.formula = formula self.instruments_formula = instruments_formula self.model = model + self.vs_prior_type = (vs_prior_type,) + self.vs_hyperparams = vs_hyperparams or {} self.input_validation() y, X = dmatrices(formula, self.data) @@ -139,7 +149,14 @@ def __init__( } self.priors = priors self.model.fit( - X=self.X, Z=self.Z, y=self.y, t=self.t, coords=COORDS, priors=self.priors + X=self.X, + Z=self.Z, + y=self.y, + t=self.t, + coords=COORDS, + priors=self.priors, + vs_prior_type=vs_prior_type, + vs_hyperparams=vs_hyperparams, ) def input_validation(self): diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 75dc14a7..18ef4264 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -26,6 +26,7 @@ from pymc_extras.prior import Prior from causalpy.utils import round_num +from causalpy.variable_selection_priors import VariableSelectionPrior class PyMCModel(pm.Model): @@ -604,7 +605,9 @@ class InstrumentalVariableRegression(PyMCModel): Inference data... """ - def build_model(self, X, Z, y, t, coords, priors): + def build_model( + self, X, Z, y, t, coords, priors, vs_prior_type=None, vs_hyperparams=None + ): """Specify model with treatment regression and focal regression data and priors :param X: A pandas dataframe used to predict our outcome y @@ -618,23 +621,47 @@ def build_model(self, X, Z, y, t, coords, priors): sigmas of both regressions :code:`priors = {"mus": [0, 0], "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}` + :param vs_prior_type: An optional string. Can be "spike_and_slab" + or "horseshoe" or "normal + :param vs_hyperparams: An optional dictionary of priors for the + variable selection hyperparameters + """ # --- Priors --- with self: self.add_coords(coords) - beta_t = pm.Normal( - name="beta_t", - mu=priors["mus"][0], - sigma=priors["sigmas"][0], - dims="instruments", - ) - beta_z = pm.Normal( - name="beta_z", - mu=priors["mus"][1], - sigma=priors["sigmas"][1], - dims="covariates", - ) + + # Create coefficient priors + if vs_prior_type: + # Use variable selection priors + vs_prior_treatment = VariableSelectionPrior( + vs_prior_type, vs_hyperparams + ) + vs_prior_outcome = VariableSelectionPrior(vs_prior_type, vs_hyperparams) + + beta_t = vs_prior_treatment.create_prior( + name="beta_t", n_params=Z.shape[1], dims="instruments", X=Z + ) + + beta_z = vs_prior_outcome.create_prior( + name="beta_z", n_params=X.shape[1], dims="covariates", X=X + ) + else: + # Use standard normal priors + beta_t = pm.Normal( + name="beta_t", + mu=priors["mus"][0], + sigma=priors["sigmas"][0], + dims="instruments", + ) + beta_z = pm.Normal( + name="beta_z", + mu=priors["mus"][1], + sigma=priors["sigmas"][1], + dims="covariates", + ) + sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2) chol, corr, sigmas = pm.LKJCholeskyCov( name="chol_cov", @@ -689,7 +716,18 @@ def sample_predictive_distribution(self, ppc_sampler="jax"): ) ) - def fit(self, X, Z, y, t, coords, priors, ppc_sampler=None): + def fit( + self, + X, + Z, + y, + t, + coords, + priors, + ppc_sampler=None, + vs_prior_type=None, + vs_hyperparams=None, + ): """Draw samples from posterior distribution and potentially from the prior and posterior predictive distributions. The fit call can take values for the @@ -703,7 +741,7 @@ def fit(self, X, Z, y, t, coords, priors, ppc_sampler=None): # sample_posterior_predictive() if provided in sample_kwargs. # Use JAX for ppc sampling of multivariate likelihood - self.build_model(X, Z, y, t, coords, priors) + self.build_model(X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams) with self: self.idata = pm.sample(**self.sample_kwargs) self.sample_predictive_distribution(ppc_sampler=ppc_sampler) diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py index e7795522..e3a1a1b9 100644 --- a/causalpy/tests/test_integration_pymc_examples.py +++ b/causalpy/tests/test_integration_pymc_examples.py @@ -676,6 +676,38 @@ def test_iv_reg(mock_pymc_sample): result.get_plot_data() +@pytest.mark.integration +def test_iv_reg_vs_prior(mock_pymc_sample): + df = cp.load_data("risk") + instruments_formula = "risk ~ 1 + logmort0" + formula = "loggdp ~ 1 + risk" + instruments_data = df[["risk", "logmort0"]] + data = df[["loggdp", "risk"]] + + result = cp.InstrumentalVariable( + instruments_data=instruments_data, + data=data, + instruments_formula=instruments_formula, + formula=formula, + model=cp.pymc_models.InstrumentalVariableRegression( + sample_kwargs=sample_kwargs + ), + vs_prior_type="spike_and_slab", + vs_hyperparams={"pi_alpha": 5}, + ) + result.model.sample_predictive_distribution(ppc_sampler="pymc") + assert isinstance(df, pd.DataFrame) + assert isinstance(data, pd.DataFrame) + assert isinstance(instruments_data, pd.DataFrame) + assert isinstance(result, cp.InstrumentalVariable) + assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"] + assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"] + with pytest.raises(NotImplementedError): + result.get_plot_data() + assert "gamma_beta_t" in result.model.named_vars + assert "pi_beta_t" in result.model.named_vars + + @pytest.mark.integration def test_inverse_prop(mock_pymc_sample): """Test the InversePropensityWeighting class.""" diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py new file mode 100644 index 00000000..5cac5013 --- /dev/null +++ b/causalpy/variable_selection_priors.py @@ -0,0 +1,591 @@ +# Copyright 2022 - 2025 The PyMC Labs Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Generic variable selection priors for PyMC models using pymc-extras Prior class. + +This module provides reusable prior specifications that can be applied to any +PyMC model with coefficient vectors (beta parameters). Supports spike-and-slab +and horseshoe priors for automatic variable selection and shrinkage, built on +top of the pymc-extras Prior infrastructure. +""" + +from typing import Any, Dict, Optional, Union + +import numpy as np +import pymc as pm +import pytensor.tensor as pt +from pymc_extras.prior import Prior + + +def _relaxed_bernoulli_transform( + p: Union[float, pt.TensorVariable], temperature: float = 0.1 +): + """ + Transform function for relaxed (continuous) Bernoulli distribution. + + This provides a continuous approximation to a Bernoulli distribution, + useful for gradient-based inference. As temperature → 0, this approaches + a true binary distribution. + + Parameters + ---------- + p : float or PyMC variable + Probability parameter + temperature : float, default=0.1 + Temperature parameter (lower = more binary) + + Returns + ------- + function + Transform function that takes uniform random variable + """ + + def transform(u): + logit_p = pt.log(p) - pt.log(1 - p) + return pm.math.sigmoid((logit_p + pt.log(u) - pt.log(1 - u)) / temperature) + + return transform + + +class SpikeAndSlabPrior: + """ + Spike-and-slab prior using pymc-extras Prior class. + + Creates a mixture prior with a point mass at zero (spike) and a diffuse + normal distribution (slab), implemented as: + + β_j = γ_j × β_j^raw + + where γ_j ∈ [0,1] is a relaxed indicator and β_j^raw ~ N(0, σ_slab²). + + Parameters + ---------- + pi_alpha : float, default=2 + Beta prior alpha for selection probability + pi_beta : float, default=2 + Beta prior beta for selection probability + slab_sigma : float, default=2 + Standard deviation of slab (non-zero) component + temperature : float, default=0.1 + Relaxation parameter for binary approximation (lower = more binary) + dims : str or tuple, optional + Dimension names for the coefficient vector + + Example + ------- + >>> spike_slab = SpikeAndSlabPrior(dims="features") + >>> with pm.Model(): + ... beta = spike_slab.create_variable("beta") + """ + + def __init__( + self, + pi_alpha: float = 2, + pi_beta: float = 2, + slab_sigma: float = 2, + temperature: float = 0.1, + dims: Optional[Union[str, tuple]] = None, + ): + self.pi_alpha = pi_alpha + self.pi_beta = pi_beta + self.slab_sigma = slab_sigma + self.temperature = temperature + self.dims = dims if isinstance(dims, tuple) or dims is None else (dims,) + + def create_variable(self, name: str) -> pm.Deterministic: + """ + Create spike-and-slab variable. + + Parameters + ---------- + name : str + Name for the coefficient vector + + Returns + ------- + pm.Deterministic + Coefficient vector with spike-and-slab prior + """ + # Selection probability using Prior class + pi_prior = Prior("Beta", alpha=self.pi_alpha, beta=self.pi_beta) + pi = pi_prior.create_variable(f"pi_{name}") + + # Raw coefficients (slab component) using Prior class + slab_prior = Prior("Normal", mu=0, sigma=self.slab_sigma, dims=self.dims) + beta_raw = slab_prior.create_variable(f"{name}_raw") + + # Selection indicators using relaxed Bernoulli + # We use Uniform and transform it + u = pm.Uniform(f"gamma_{name}_u", 0, 1, dims=self.dims) + transform_fn = _relaxed_bernoulli_transform(pi, self.temperature) + gamma = pm.Deterministic(f"gamma_{name}", transform_fn(u), dims=self.dims) + + # Actual coefficients + return pm.Deterministic(name, gamma * beta_raw, dims=self.dims) + + +class HorseshoePrior: + """ + Regularized horseshoe prior using pymc-extras Prior class. + + Provides continuous shrinkage with heavy tails, allowing strong signals + to escape shrinkage while weak signals are dampened: + + β_j = τ · λ̃_j · β_j^raw + + where λ̃_j = √(c²λ_j² / (c² + τ²λ_j²)) is the regularized local shrinkage. + + Parameters + ---------- + tau0 : float, optional + Global shrinkage parameter. If None, computed from data. + nu : float, default=3 + Degrees of freedom for half-t prior on tau + c2_alpha : float, default=2 + InverseGamma alpha for regularization parameter + c2_beta : float, default=2 + InverseGamma beta for regularization parameter + dims : str or tuple, optional + Dimension names for the coefficient vector + + Example + ------- + >>> horseshoe = HorseshoePrior(dims="features") + >>> with pm.Model(): + ... beta = horseshoe.create_variable("beta") + """ + + def __init__( + self, + tau0: Optional[float] = None, + nu: float = 3, + c2_alpha: float = 2, + c2_beta: float = 2, + dims: Optional[Union[str, tuple]] = None, + ): + self.tau0 = tau0 + self.nu = nu + self.c2_alpha = c2_alpha + self.c2_beta = c2_beta + self.dims = dims if isinstance(dims, tuple) or dims is None else (dims,) + + def create_variable(self, name: str) -> pm.Deterministic: + """ + Create horseshoe variable. + + Parameters + ---------- + name : str + Name for the coefficient vector + + Returns + ------- + pm.Deterministic + Coefficient vector with horseshoe prior + """ + # Global shrinkage using Prior class + tau_prior = Prior("HalfStudentT", nu=self.nu, sigma=self.tau0 or 1.0) + tau = tau_prior.create_variable(f"tau_{name}") + + # Local shrinkage parameters using Prior class + lambda_prior = Prior("HalfCauchy", beta=1.0, dims=self.dims) + lambda_ = lambda_prior.create_variable(f"lambda_{name}") + + # Regularization parameter using Prior class + c2_prior = Prior("InverseGamma", alpha=self.c2_alpha, beta=self.c2_beta) + c2 = c2_prior.create_variable(f"c2_{name}") + + # Regularized local shrinkage + lambda_tilde = pm.Deterministic( + f"lambda_tilde_{name}", + pm.math.sqrt(c2 * lambda_**2 / (c2 + tau**2 * lambda_**2)), + dims=self.dims, + ) + + # Raw coefficients using Prior class + raw_prior = Prior("Normal", mu=0, sigma=1, dims=self.dims) + beta_raw = raw_prior.create_variable(f"{name}_raw") + + # Actual coefficients + return pm.Deterministic(name, beta_raw * lambda_tilde * tau, dims=self.dims) + + +class VariableSelectionPrior: + """ + Factory for creating variable selection priors on coefficient vectors. + + This class provides a unified interface for different types of variable + selection priors that can be applied to any beta coefficient in a PyMC model. + Built on top of pymc-extras Prior class for consistency and interoperability. + + Supported prior types: + - 'spike_and_slab': Mixture prior with near-zero spike and diffuse slab + - 'horseshoe': Continuous shrinkage with adaptive regularization + - 'normal': Standard normal prior (no selection, for comparison) + + Parameters + ---------- + prior_type : str + Type of prior: 'spike_and_slab', 'horseshoe', or 'normal' + hyperparams : dict, optional + Hyperparameters specific to the chosen prior type. If None, defaults are used. + + For 'spike_and_slab': + - pi_alpha: float (default=2) - Beta prior alpha for selection probability + - pi_beta: float (default=2) - Beta prior beta for selection probability + - slab_sigma: float (default=2) - SD of slab (non-zero) component + - temperature: float (default=0.1) - Relaxation parameter for binary approximation + + For 'horseshoe': + - tau0: float (default=None) - Global shrinkage, auto-computed if None + - nu: float (default=3) - Degrees of freedom for half-t prior on tau + - c2_alpha: float (default=2) - InverseGamma alpha for regularization + - c2_beta: float (default=2) - InverseGamma beta for regularization + + For 'normal': + - mu: float or array (default=0) - Prior mean + - sigma: float or array (default=1) - Prior SD + + Example + ------- + >>> import pymc as pm + >>> from variable_selection_priors import VariableSelectionPrior + >>> + >>> # Create spike-and-slab prior + >>> vs_prior = VariableSelectionPrior("spike_and_slab") + >>> + >>> with pm.Model() as model: + ... # Create coefficients with variable selection + ... beta = vs_prior.create_prior( + ... name="beta", + ... n_params=5, + ... dims="features", + ... X=X_train, # For computing tau0 in horseshoe + ... ) + """ + + def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None): + """Initialize the variable selection prior factory.""" + self.prior_type = prior_type.lower() + self.hyperparams = hyperparams or {} + + if self.prior_type not in ["spike_and_slab", "horseshoe", "normal"]: + raise ValueError( + f"Unknown prior_type: {prior_type}. " + "Must be 'spike_and_slab', 'horseshoe', or 'normal'" + ) + + # Will be set when create_prior is called + self._prior_instance = None + + def _get_default_hyperparams( + self, n_params: int, X: Optional[np.ndarray] = None + ) -> Dict[str, Any]: + """ + Get default hyperparameters for the chosen prior type. + + Parameters + ---------- + n_params : int + Number of parameters (dimension of beta vector) + X : array-like, optional + Design matrix for computing data-adaptive defaults (horseshoe only) + + Returns + ------- + dict + Default hyperparameters + """ + if self.prior_type == "spike_and_slab": + return { + "pi_alpha": 2, + "pi_beta": 2, + "slab_sigma": 2, + "temperature": 0.1, + } + + elif self.prior_type == "horseshoe": + # Compute tau0 using rule of thumb from Piironen & Vehtari (2017) + if X is not None: + p = n_params + p0 = min(5.0, p / 2) # Expected number of nonzero coefficients + sigma_est = 1.0 + n = X.shape[0] + tau0 = (p0 / (p - p0)) * (sigma_est / np.sqrt(n)) + else: + # Fallback if no data provided + tau0 = 1.0 / np.sqrt(n_params) + + return { + "tau0": tau0, + "nu": 3, + "c2_alpha": 2, + "c2_beta": 2, + } + + else: # normal + return { + "mu": 0, + "sigma": 1, + } + + def create_prior( + self, + name: str, + n_params: int, + dims: Optional[Union[str, tuple]] = None, + X: Optional[np.ndarray] = None, + hyperparams: Optional[Dict[str, Any]] = None, + ) -> Union[pm.Deterministic, pm.Distribution]: + """ + Create the specified prior on a coefficient vector. + + This is the main method to use. It creates the appropriate prior type + based on the configuration and returns the PyMC variable. + + Parameters + ---------- + name : str + Name for the coefficient vector (e.g., 'beta', 'b', 'coef') + n_params : int + Number of parameters (length of coefficient vector) + dims : str or tuple, optional + Dimension name(s) for the coefficient vector + X : array-like, optional + Design matrix for computing data-adaptive hyperparameters + (used only for horseshoe priors) + hyperparams : dict, optional + Override default hyperparameters for this specific prior instance + + Returns + ------- + PyMC variable + The coefficient vector with the specified prior + + Example + ------- + >>> vs_prior = VariableSelectionPrior("horseshoe") + >>> with pm.Model() as model: + ... beta = vs_prior.create_prior( + ... "beta", n_params=10, dims="features", X=X_train + ... ) + """ + # Merge instance and call-specific hyperparameters + default_hp = self._get_default_hyperparams(n_params, X) + merged_hp = {**default_hp, **self.hyperparams} + if hyperparams: + merged_hp.update(hyperparams) + + # Normalize dims + if isinstance(dims, str): + dims = (dims,) + + # Create the appropriate prior + if self.prior_type == "spike_and_slab": + self._prior_instance = SpikeAndSlabPrior( + pi_alpha=merged_hp["pi_alpha"], + pi_beta=merged_hp["pi_beta"], + slab_sigma=merged_hp["slab_sigma"], + temperature=merged_hp["temperature"], + dims=dims, + ) + return self._prior_instance.create_variable(name) + + elif self.prior_type == "horseshoe": + self._prior_instance = HorseshoePrior( + tau0=merged_hp["tau0"], + nu=merged_hp["nu"], + c2_alpha=merged_hp["c2_alpha"], + c2_beta=merged_hp["c2_beta"], + dims=dims, + ) + return self._prior_instance.create_variable(name) + + else: # normal + # Use Prior class directly for normal + normal_prior = Prior( + "Normal", mu=merged_hp["mu"], sigma=merged_hp["sigma"], dims=dims + ) + return normal_prior.create_variable(name) + + def get_inclusion_probabilities( + self, idata, param_name: str, threshold: float = 0.5 + ) -> Dict[str, np.ndarray]: + """ + Extract variable inclusion probabilities from fitted model. + + Only applicable for spike-and-slab priors. Returns the posterior + probability that each coefficient is "selected" (non-zero). + + Parameters + ---------- + idata : arviz.InferenceData + Fitted model inference data + param_name : str + Name of the coefficient parameter (must match name in create_prior) + threshold : float, default=0.5 + Threshold for considering a variable "selected" + + Returns + ------- + dict + Dictionary with keys: + - 'probabilities': Array of inclusion probabilities per coefficient + - 'selected': Boolean array indicating which are selected + - 'gamma_mean': Mean of gamma (indicator) variables + + Raises + ------ + ValueError + If prior_type is not 'spike_and_slab' or gamma variables not found + + Example + ------- + >>> result = vs_prior.get_inclusion_probabilities(idata, "beta") + >>> print(f"Selected features: {result['selected']}") + >>> print(f"Inclusion probs: {result['probabilities']}") + """ + if self.prior_type != "spike_and_slab": + raise ValueError( + "Inclusion probabilities only available for 'spike_and_slab' priors" + ) + + gamma_name = f"gamma_{param_name}" + + if gamma_name not in idata.posterior: + raise ValueError( + f"Could not find '{gamma_name}' in posterior. " + f"Make sure you used the correct parameter name." + ) + + import arviz as az + + # Extract gamma values + gamma = az.extract(idata.posterior[gamma_name]) + + # Compute inclusion probabilities + probabilities = (gamma > threshold).mean(dim="sample").values + gamma_mean = gamma.mean(dim="sample").values + selected = probabilities > threshold + + return { + "probabilities": probabilities, + "selected": selected, + "gamma_mean": gamma_mean, + } + + def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]: + """ + Extract shrinkage factors from horseshoe prior. + + Only applicable for horseshoe priors. Returns the effective shrinkage + applied to each coefficient: κ_j = τ · λ̃_j + + Parameters + ---------- + idata : arviz.InferenceData + Fitted model inference data + param_name : str + Name of the coefficient parameter + + Returns + ------- + dict + Dictionary with keys: + - 'shrinkage_factors': Array of shrinkage factors per coefficient + - 'tau': Global shrinkage parameter + - 'lambda_tilde': Regularized local shrinkage parameters + + Raises + ------ + ValueError + If prior_type is not 'horseshoe' or required variables not found + + Example + ------- + >>> result = vs_prior.get_shrinkage_factors(idata, "beta") + >>> print(f"Shrinkage factors: {result['shrinkage_factors']}") + """ + if self.prior_type != "horseshoe": + raise ValueError("Shrinkage factors only available for 'horseshoe' priors") + + import arviz as az + + tau_name = f"tau_{param_name}" + lambda_tilde_name = f"lambda_tilde_{param_name}" + + if tau_name not in idata.posterior: + raise ValueError(f"Could not find '{tau_name}' in posterior") + if lambda_tilde_name not in idata.posterior: + raise ValueError(f"Could not find '{lambda_tilde_name}' in posterior") + + # Extract components + tau = az.extract(idata.posterior[tau_name]) + lambda_tilde = az.extract(idata.posterior[lambda_tilde_name]) + + # Compute shrinkage factors + shrinkage_factors = (tau * lambda_tilde).mean(dim="sample").values + + return { + "shrinkage_factors": shrinkage_factors, + "tau": tau.mean().values, + "lambda_tilde": lambda_tilde.mean(dim="sample").values, + } + + +def create_variable_selection_prior( + prior_type: str, + name: str, + n_params: int, + dims: Optional[Union[str, tuple]] = None, + X: Optional[np.ndarray] = None, + hyperparams: Optional[Dict[str, Any]] = None, +) -> Union[pm.Deterministic, pm.Distribution]: + """ + Convenience function to create a variable selection prior in one call. + + This is a shorthand for creating a VariableSelectionPrior instance and + calling create_prior() in one step. + + Parameters + ---------- + prior_type : str + Type of prior: 'spike_and_slab', 'horseshoe', or 'normal' + name : str + Name for the coefficient vector + n_params : int + Number of parameters + dims : str or tuple, optional + Dimension name(s) + X : array-like, optional + Design matrix for data-adaptive hyperparameters + hyperparams : dict, optional + Custom hyperparameters + + Returns + ------- + PyMC variable + The coefficient vector with specified prior + + Example + ------- + >>> with pm.Model() as model: + ... X = pm.Data("X", X_train) + ... beta = create_variable_selection_prior( + ... "spike_and_slab", "beta", n_params=X_train.shape[1], dims="features" + ... ) + ... mu = pm.math.dot(X, beta) + """ + vs_prior = VariableSelectionPrior(prior_type, hyperparams) + return vs_prior.create_prior(name, n_params, dims, X) diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg index 392a876b..a00d0758 100644 --- a/docs/source/_static/interrogate_badge.svg +++ b/docs/source/_static/interrogate_badge.svg @@ -1,5 +1,5 @@ - interrogate: 96.2% + interrogate: 95.7% @@ -12,8 +12,8 @@ interrogate interrogate - 96.2% - 96.2% + 95.7% + 95.7% From db1e81d2caf76bbee4a5d136c877c11327d8bf01 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Thu, 20 Nov 2025 07:02:23 +0000 Subject: [PATCH 02/24] adding demo notebook Signed-off-by: Nathaniel --- causalpy/experiments/instrumental_variable.py | 14 +- causalpy/pymc_models.py | 8 + docs/source/notebooks/iv_vs_priors.ipynb | 1377 +++++++++++++++++ 3 files changed, 1397 insertions(+), 2 deletions(-) create mode 100644 docs/source/notebooks/iv_vs_priors.ipynb diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py index d24a54be..67852123 100644 --- a/causalpy/experiments/instrumental_variable.py +++ b/causalpy/experiments/instrumental_variable.py @@ -91,6 +91,16 @@ class InstrumentalVariable(BaseExperiment): ... formula=formula, ... model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs), ... ) + >>> # With variable selection + >>> iv = cp.InstrumentalVariable( + ... instruments_data=instruments_data, + ... data=data, + ... instruments_formula=instruments_formula, + ... formula=formula, + ... model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs), + ... vs_prior_type="spike_and_slab", + ... vs_hyperparams={"slab_sigma": 5.0}, + ... ) """ supports_ols = False @@ -115,7 +125,7 @@ def __init__( self.formula = formula self.instruments_formula = instruments_formula self.model = model - self.vs_prior_type = (vs_prior_type,) + self.vs_prior_type = vs_prior_type self.vs_hyperparams = vs_hyperparams or {} self.input_validation() @@ -142,7 +152,7 @@ def __init__( if priors is None: priors = { "mus": [self.ols_beta_first_params, self.ols_beta_second_params], - "sigmas": [1, 1], + "sigmas": [10, 10], "eta": 2, "lkj_sd": 1, } diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index f31e147e..d0c04bfd 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -13,6 +13,7 @@ # limitations under the License. """Custom PyMC models for causal inference""" +import warnings from typing import Any, Dict import arviz as az @@ -694,6 +695,13 @@ def build_model( # type: ignore with self: self.add_coords(coords) + if vs_prior_type and ("mus" in priors or "sigmas" in priors): + warnings.warn( + "Variable selection priors specified. " + "The 'mus' and 'sigmas' in the priors dict will be ignored " + "for beta coefficients. Only 'eta' and 'lkj_sd' will be used." + ) + # Create coefficient priors if vs_prior_type: # Use variable selection priors diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb new file mode 100644 index 00000000..6823d382 --- /dev/null +++ b/docs/source/notebooks/iv_vs_priors.ipynb @@ -0,0 +1,1377 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 42, + "id": "532c6736", + "metadata": {}, + "outputs": [], + "source": [ + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import causalpy as cp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "046aa8e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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X.mean(axis=0)) / X.std(axis=0)\n", + "\n", + " mean = [0, 0]\n", + " cov = [[sigma_U**2, rho * sigma_U * sigma_V], [rho * sigma_U * sigma_V, sigma_V**2]]\n", + " errors = np.random.multivariate_normal(mean, cov, size=n)\n", + " U = errors[:, 0] # error in outcome equation\n", + " V = errors[:, 1] #\n", + "\n", + " # continuous treatment\n", + " T_cont = X @ betaD + V\n", + "\n", + " # latent variable for binary treatment\n", + " T_latent = X @ betaD + V\n", + " T_bin = np.random.binomial(n=1, p=inv_logit(T_latent), size=n)\n", + "\n", + " alpha_individual = 3.0 + 2.5 * X[:, 0]\n", + "\n", + " # outcomes\n", + " Y_cont = alpha_true * T_cont + X @ betaY + U\n", + " if cate_estimation:\n", + " Y_bin = alpha_individual * T_bin + X @ betaY + U\n", + " else:\n", + " Y_bin = alpha_true * T_bin + X @ betaY + U\n", + "\n", + " # combine into DataFrame\n", + " data = pd.DataFrame(\n", + " {\n", + " \"Y_cont\": Y_cont,\n", + " \"Y_bin\": Y_bin,\n", + " \"T_cont\": T_cont,\n", + " \"T_bin\": T_bin,\n", + " }\n", + " )\n", + " data[\"alpha\"] = alpha_true + alpha_individual\n", + " for j in range(p):\n", + " data[f\"feature_{j}\"] = X[:, j]\n", + " data[\"Y_cont_scaled\"] = (data[\"Y_cont\"] - data[\"Y_cont\"].mean()) / data[\n", + " \"Y_cont\"\n", + " ].std(ddof=1)\n", + " data[\"Y_bin_scaled\"] = (data[\"Y_bin\"] - data[\"Y_bin\"].mean()) / data[\"Y_bin\"].std(\n", + " ddof=1\n", + " )\n", + " data[\"T_cont_scaled\"] = (data[\"T_cont\"] - data[\"T_cont\"].mean()) / data[\n", + " \"T_cont\"\n", + " ].std(ddof=1)\n", + " data[\"T_bin_scaled\"] = (data[\"T_bin\"] - data[\"T_bin\"].mean()) / data[\"T_bin\"].std(\n", + " ddof=1\n", + " )\n", + " return data\n", + "\n", + "\n", + "data = simulate_data()\n", + "instruments_data = data.copy()\n", + "features = [col for col in data.columns if \"feature\" in col]\n", + "formula = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n", + "instruments_formula = \"T_cont ~ 1 + \" + \" + \".join(features)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "763ca253", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------------------------------\n", + "Model 1: Normal Priors (No Variable Selection)\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n", + " This is not necessarily a problem but it violates\n", + " the assumption of a simple IV experiment.\n", + " The coefficients should be interpreted appropriately.\n", + " \"\"\"Validate the input data and model formula for correctness\"\"\"\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (2 chains in 2 jobs)\n", + "NUTS: [beta_t, beta_z, chol_cov]\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "75ceaa4f49be4429bdb3671caae6d44f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n", + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n" + ] + }, + { + "data": { + "text/html": [ + "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 69 seconds.\n",
+      "There were 17 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                This is not necessarily a problem but it violates\n",
+      "                the assumption of a simple IV experiment.\n",
+      "                The coefficients should be interpreted appropriately.\n",
+      "  \"\"\"Validate the input data and model formula for correctness\"\"\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 2: Spike-and-Slab Priors\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "NUTS: [pi_beta_t, beta_t_raw, gamma_beta_t_u, pi_beta_z, beta_z_raw, gamma_beta_z_u, chol_cov]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8478b37ed9ea450c9d18f31ceacc9c16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 316 seconds.\n",
+      "There were 86 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_kwargs = {\n",
+    "    \"draws\": 1000,\n",
+    "    \"tune\": 2000,\n",
+    "    \"chains\": 2,\n",
+    "    \"cores\": 2,\n",
+    "    \"mptarget_accept\": 0.95,\n",
+    "    \"progressbar\": True,\n",
+    "    \"random_seed\": 42,\n",
+    "    \"mp_ctx\": \"spawn\",\n",
+    "}\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 1: Normal priors (no selection)\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 1: Normal Priors (No Variable Selection)\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_normal = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=None,  # No variable selection\n",
+    ")\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 2: Spike-and-Slab priors\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 2: Spike-and-Slab Priors\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_spike_slab = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=\"spike_and_slab\",\n",
+    "    vs_hyperparams={\n",
+    "        \"pi_alpha\": 2,\n",
+    "        \"pi_beta\": 2,\n",
+    "        \"slab_sigma\": 2,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "a9633162",
+   "metadata": {},
+   "outputs": [
+    {
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
beta_z[Intercept]0.0150.059-0.0980.1250.0010.0021658.01023.01.00
beta_z[T_cont]3.1540.3572.4293.7770.0290.011152.0373.01.01
beta_z[feature_0]0.4300.2330.0170.9050.0190.007159.0436.01.01
beta_z[feature_1]-0.3830.059-0.491-0.2770.0010.0021720.01250.01.00
beta_z[feature_2]0.0300.162-0.2720.3410.0120.005172.0418.01.01
beta_z[feature_3]-0.0550.143-0.3010.2420.0110.005171.0428.01.00
beta_z[feature_4]0.4420.0640.3350.5640.0030.004685.0668.01.00
beta_z[feature_5]-0.0000.058-0.1010.1130.0020.0021338.0970.01.00
beta_z[feature_6]0.0270.061-0.0940.1400.0020.0021184.01063.01.00
beta_z[feature_7]-0.0370.058-0.1400.0770.0020.001914.0675.01.00
beta_z[feature_8]-0.0760.060-0.1930.0340.0020.002827.0602.01.00
beta_z[feature_9]0.0420.060-0.0720.1460.0020.0041191.0809.01.00
beta_z[feature_10]-0.0340.061-0.1510.0770.0020.002776.0907.01.00
beta_z[feature_11]0.0750.059-0.0290.1900.0020.001864.0904.01.00
beta_z[feature_12]-0.0450.061-0.1600.0640.0020.002951.0946.01.00
beta_z[feature_13]-0.0400.059-0.1640.0600.0020.0021466.01266.01.00
\n",
+       "
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+      ],
+      "text/plain": [
+       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "beta_z[Intercept]   0.015  0.059  -0.098    0.125      0.001    0.002   \n",
+       "beta_z[T_cont]      3.154  0.357   2.429    3.777      0.029    0.011   \n",
+       "beta_z[feature_0]   0.430  0.233   0.017    0.905      0.019    0.007   \n",
+       "beta_z[feature_1]  -0.383  0.059  -0.491   -0.277      0.001    0.002   \n",
+       "beta_z[feature_2]   0.030  0.162  -0.272    0.341      0.012    0.005   \n",
+       "beta_z[feature_3]  -0.055  0.143  -0.301    0.242      0.011    0.005   \n",
+       "beta_z[feature_4]   0.442  0.064   0.335    0.564      0.003    0.004   \n",
+       "beta_z[feature_5]  -0.000  0.058  -0.101    0.113      0.002    0.002   \n",
+       "beta_z[feature_6]   0.027  0.061  -0.094    0.140      0.002    0.002   \n",
+       "beta_z[feature_7]  -0.037  0.058  -0.140    0.077      0.002    0.001   \n",
+       "beta_z[feature_8]  -0.076  0.060  -0.193    0.034      0.002    0.002   \n",
+       "beta_z[feature_9]   0.042  0.060  -0.072    0.146      0.002    0.004   \n",
+       "beta_z[feature_10] -0.034  0.061  -0.151    0.077      0.002    0.002   \n",
+       "beta_z[feature_11]  0.075  0.059  -0.029    0.190      0.002    0.001   \n",
+       "beta_z[feature_12] -0.045  0.061  -0.160    0.064      0.002    0.002   \n",
+       "beta_z[feature_13] -0.040  0.059  -0.164    0.060      0.002    0.002   \n",
+       "\n",
+       "                    ess_bulk  ess_tail  r_hat  \n",
+       "beta_z[Intercept]     1658.0    1023.0   1.00  \n",
+       "beta_z[T_cont]         152.0     373.0   1.01  \n",
+       "beta_z[feature_0]      159.0     436.0   1.01  \n",
+       "beta_z[feature_1]     1720.0    1250.0   1.00  \n",
+       "beta_z[feature_2]      172.0     418.0   1.01  \n",
+       "beta_z[feature_3]      171.0     428.0   1.00  \n",
+       "beta_z[feature_4]      685.0     668.0   1.00  \n",
+       "beta_z[feature_5]     1338.0     970.0   1.00  \n",
+       "beta_z[feature_6]     1184.0    1063.0   1.00  \n",
+       "beta_z[feature_7]      914.0     675.0   1.00  \n",
+       "beta_z[feature_8]      827.0     602.0   1.00  \n",
+       "beta_z[feature_9]     1191.0     809.0   1.00  \n",
+       "beta_z[feature_10]     776.0     907.0   1.00  \n",
+       "beta_z[feature_11]     864.0     904.0   1.00  \n",
+       "beta_z[feature_12]     951.0     946.0   1.00  \n",
+       "beta_z[feature_13]    1466.0    1266.0   1.00  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(result_normal.idata, var_names=[\"beta_z\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "bfcdeecd",
+   "metadata": {},
+   "outputs": [
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
beta_z[Intercept]0.0010.010-0.0150.0110.0000.0011035.0807.01.00
beta_z[T_cont]3.0760.0992.9003.2690.0060.004286.0340.01.00
beta_z[feature_0]0.4760.0860.3230.6480.0040.002477.0571.01.00
beta_z[feature_1]-0.3920.047-0.480-0.3060.0010.0011903.01459.01.00
beta_z[feature_2]-0.0000.018-0.0230.0190.0010.002464.0886.01.01
beta_z[feature_3]-0.0030.021-0.0310.0180.0010.003357.0313.01.00
beta_z[feature_4]0.4230.0490.3410.5200.0030.001380.0176.01.01
beta_z[feature_5]0.0000.009-0.0120.0080.0000.001629.01103.01.00
beta_z[feature_6]0.0030.016-0.0120.0330.0000.001781.01101.01.00
beta_z[feature_7]-0.0050.019-0.0380.0150.0010.001738.0472.01.01
beta_z[feature_8]-0.0040.018-0.0390.0120.0010.001700.0973.01.00
beta_z[feature_9]0.0020.014-0.0120.0130.0000.001925.01187.01.00
beta_z[feature_10]-0.0010.010-0.0110.0120.0000.0011407.01059.01.00
beta_z[feature_11]0.0040.019-0.0160.0310.0010.001787.0727.01.01
beta_z[feature_12]-0.0060.023-0.0500.0100.0010.001394.0684.01.01
beta_z[feature_13]-0.0010.011-0.0120.0050.0000.001746.01345.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "beta_z[Intercept]   0.001  0.010  -0.015    0.011      0.000    0.001   \n",
+       "beta_z[T_cont]      3.076  0.099   2.900    3.269      0.006    0.004   \n",
+       "beta_z[feature_0]   0.476  0.086   0.323    0.648      0.004    0.002   \n",
+       "beta_z[feature_1]  -0.392  0.047  -0.480   -0.306      0.001    0.001   \n",
+       "beta_z[feature_2]  -0.000  0.018  -0.023    0.019      0.001    0.002   \n",
+       "beta_z[feature_3]  -0.003  0.021  -0.031    0.018      0.001    0.003   \n",
+       "beta_z[feature_4]   0.423  0.049   0.341    0.520      0.003    0.001   \n",
+       "beta_z[feature_5]   0.000  0.009  -0.012    0.008      0.000    0.001   \n",
+       "beta_z[feature_6]   0.003  0.016  -0.012    0.033      0.000    0.001   \n",
+       "beta_z[feature_7]  -0.005  0.019  -0.038    0.015      0.001    0.001   \n",
+       "beta_z[feature_8]  -0.004  0.018  -0.039    0.012      0.001    0.001   \n",
+       "beta_z[feature_9]   0.002  0.014  -0.012    0.013      0.000    0.001   \n",
+       "beta_z[feature_10] -0.001  0.010  -0.011    0.012      0.000    0.001   \n",
+       "beta_z[feature_11]  0.004  0.019  -0.016    0.031      0.001    0.001   \n",
+       "beta_z[feature_12] -0.006  0.023  -0.050    0.010      0.001    0.001   \n",
+       "beta_z[feature_13] -0.001  0.011  -0.012    0.005      0.000    0.001   \n",
+       "\n",
+       "                    ess_bulk  ess_tail  r_hat  \n",
+       "beta_z[Intercept]     1035.0     807.0   1.00  \n",
+       "beta_z[T_cont]         286.0     340.0   1.00  \n",
+       "beta_z[feature_0]      477.0     571.0   1.00  \n",
+       "beta_z[feature_1]     1903.0    1459.0   1.00  \n",
+       "beta_z[feature_2]      464.0     886.0   1.01  \n",
+       "beta_z[feature_3]      357.0     313.0   1.00  \n",
+       "beta_z[feature_4]      380.0     176.0   1.01  \n",
+       "beta_z[feature_5]      629.0    1103.0   1.00  \n",
+       "beta_z[feature_6]      781.0    1101.0   1.00  \n",
+       "beta_z[feature_7]      738.0     472.0   1.01  \n",
+       "beta_z[feature_8]      700.0     973.0   1.00  \n",
+       "beta_z[feature_9]      925.0    1187.0   1.00  \n",
+       "beta_z[feature_10]    1407.0    1059.0   1.00  \n",
+       "beta_z[feature_11]     787.0     727.0   1.01  \n",
+       "beta_z[feature_12]     394.0     684.0   1.01  \n",
+       "beta_z[feature_13]     746.0    1345.0   1.00  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(result_spike_slab.idata, var_names=[\"beta_z\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_spike_slab.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "127888b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([], dtype=object)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09b24bf",
+   "metadata": {},
+   "source": [
+    "## The Treatment Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "acafc928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([], dtype=object)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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l1ahPS0tLKZEEAHt7ezg5OWkkhfb29hrT2Dk5OZg4cSJkMhm6dOkCPz8/AMDFixfv6/qII5NERETtRyfLhhFCQ51bDz4+PnjuueewZMkSjRFHURQb3dktNjGV3rlz49HQTp06abwWBKHJsvr6egBAdXU1xowZgzFjxuDf//43/vSnP+HixYt47rnneFNPK2AySURE1F4Igs5TzQ+DVatWwcPDA3379pXKBgwYgGPHjmm0O3HiBPr27Xtfz4luSn5+Pq5evYpVq1ahV69eAIBvv/22Vc/xKOM0NxEREbUpd3d3TJs2DZs3b5bK/va3vyE1NRUrVqzA2bNnsWPHDmzZskVjfWRr6d27N0xNTbF582ZcuHABycnJWLFiRauf51HFZJKIiIja3IoVKzSmsZ9++mns2bMHu3fvhpubG95++20sX768Te6w/tOf/gS5XI69e/diwIABWLVqFdauXdvq53lUCWJTCxSIiIjIoGpqalBUVARnZ2eYm5sbOhzqgFrrM8aRSSIiIiLSG2/AISJ6hJz77TqU127AydYSfey7GDocIuoAmEwSET0CyqtrMT8uB8fOX5XKnnGxw6aXPNG9s6kBIyOi9o7T3EREj4C/7MrGz7/+Dx+8/DROvTUKH7z8NH7+9X94fXeOoUMjonZO52RSoVBAEARER0e3QTgPJ0EQNL5qamoMHdIj45tvvtF479VPLCAi7Z377ToyL1zD8oluGD/QET26mGP8QEfETHRFxrmrOPfbdUOHSETt2EM3Munk5AQnJ6cHes7o6GgIggCFQtFsG5lMhqioKERFRcHE5I/VAerkeu7cuW0eQ0d1ryTxiSeekN53ItKP8toNAMAQZxuN8qHO3TXqiYj0wTWTWnJycnqkRmMfFk888YT0vsfExBg2GKJ2ysm24TF4p4sqMH6go1R+qqhco56ISB9MJomIOrg+9l3wjIsd3k7KgwgRQ52741RROaKSfsSf+9jxrm4iui/3Nc199OhR+Pr6wsrKCt27d8fLL7+MS5cuNWpXWlqKN954Ay4uLjAzM4OdnR1CQkKQl5cntVEqlRAEAcXFxSguLtZYJ6cemaqtrcXmzZvx3HPPoVevXjAzM0OPHj0wefJk5OTot4jcz89PGvHy9/eXznm/U+1+fn4QBAEqlQorVqyAs7MzzMzM0LdvX3z44Yd6xaDN+6imXi5QWVmJ+fPno1evXjAxMYFcLpfa/PDDD3jllVfQs2dPmJmZwdHREYGBgfjyyy8b9ZeUlIRRo0bBxsYG5ubmcHNzw9q1a1FXV6fRTi6XQxAEyOVyJCQkYMiQIbC0tISDgwNeffVVVFRUSG3VSwQAID09XeNnfmecRHT/Nr3kiQGPWWPeFzkY+k4q5n2RgwGPWWPjVE9Dh0ZE7ZzeI5MnT55EbGwsxo8fj/nz5yM7OxtxcXE4duwYTp8+DXt7ewBAYWEh/Pz8cPnyZYwZMwbBwcEoLS1FfHw8Dh06hNTUVHh7e6Nbt26IiorChg0bAAALFiyQzqVeT1deXo4FCxbgz3/+M8aNGwcbGxvpGZsHDhzA0aNHMWTIEJ2uQ/3YpvT0dISFhUkJXLdu3fR9azS89NJLyMrKwtixY2FsbIw9e/bgL3/5Czp16oTZs2drHYO27+Odbt26hYCAAFy/fh1BQUEwNTWVfi4JCQl46aWXUF9fj6CgIPTr1w+lpaXIysrCtm3bEBQUJPWzZMkSxMbGomfPnggJCYG1tTWOHj2KxYsXIysrC3v37m103fv27cPXX3+NF154Ac8++yzS09Px0UcfITMzE5mZmbCwsICTkxOioqIQExMDmUym8QgtDw+P+3/ziUjSvbMpPp/pzX0mqd1QKpVwdnZGTk4OPDw8oFAo4O/vj4qKilb7Hd1W5HI5FixYgMrKygfah5OTExYsWKCRQz0Qoo6OHDkiAhABiP/617806mJiYkQAYmRkpFQ2YsQI0cTERDx8+LBG24KCArFLly6iu7u7RrlMJhNlMlmT566pqREvXbrUqDwvL0+0srISn332WV0vRxRFUYyKihIBiEeOHGmyHoDo6+vbZJ36/ZgzZ45Gua+vrwhA9Pb2FquqqqTy/Px80cTEROzXr59OMejzPgIQx4wZI964cUOj7rfffhOtrKzEzp07i9nZ2Y3O9csvv0j/ffjwYRGAOHbsWLG6uloqr6+vF+fOnSsCEPft2yeVb9++Xfp8fPPNNxr9RkREiADE5cuXa5Tf6/3Vpx0RUUdw8+ZN8aeffhJv3rxp6FB09ttvv4n/93//J/bq1Us0NTUV7e3txTFjxognTpzQug+VSiX++uuv4u3bt0VR/OP3bUVFRRtF3Xq2b98udu3a9Z5t0tLSRD8/P9HGxka0sLAQXVxcxBkzZkjXq00fd5PJZOL777+vdfvW+ozpPc3dr18/REZGapQtXrwYf/rTnxAXF4fa2lrk5OTgxIkTCAsLw+jRozXa9u3bF7Nnz0Zubm6T07RNMTMzw+OPP96o3NXVFf7+/jh69Chu376t7yW1idjYWFhbW0uv+/Xrh5EjR6KgoADXr2u3Hcf9vI/vvfceLCwsNMp27NiB33//HX/729/g6dl4iqtnz57Sf2/ZsgUA8PHHH8PS8o9F+oIgYNWqVRAEAXFxcY36GD16NEaNGqVRtnLlSnTq1Ak7duzQ4qqJiKi9CgkJwffff48dO3bg7NmzSE5Ohp+fH8rLy7Xuw9jYGA4ODho7qHQUP/74I8aOHYshQ4bg6NGjyM3NxebNm9GpUyfU19cbOjyd6f0TGjlypLTeTc3CwgKDBw/GwYMHcfbsWZw8eRIAUFJS0uSd0Pn5+dJ3Nzc3rc575swZrFmzBseOHUNJSUmj5PHq1atwdHRs5ugH7+mnn25Upk7WKisr0aVLy9NM+r6P5ubmcHd3b9T+1KlTAIAxY8Zode7OnTtj27ZtTdZbWFhI57/Tn//850Zljz32GJ588knk5+fj+vXrWl07ERG1L5WVlTh27BgUCgV8fX0BNGyvN3ToUI12giDgww8/RHJyMhQKBRwcHLBmzRq88MILABpPc9/t5s2bCA0NxbVr1/DVV1+he/fu2L59O9asWYOioiI4OTlh/vz5eO2115qN9eDBg1i5ciXy8vJgbGyM4cOHY+PGjXjyySc1YoiPj8fmzZuRlZWFPn364KOPPsLw4cOlfuRyOd5++21cvXoVzz33HJ555pl7vkdff/01HB0dsWbNGqnsySefRGBgYLPHFBYWYuHChTh58iSqq6vRv39/xMbG4tlnn9Vod/36dbz88stITk6GtbU1/vnPf+Kvf/3rPeO5X3onkz169GiyXL0mr6qqSvoLJCUlBSkpKc32VV1drdU5T5w4gYCAAAANiVCfPn1gZWUFQRCQmJiI77//Hrdu3dLlMtpc165dG5Wp/8q6++aV5uj7Pvbo0aNRwg9AWn/R1ChvU+dWqVT33JanqZ/fvT4f+fn5+N///sdkkohIR6Io4uZt7X53tDaLTsZN/k65m5WVFaysrJCYmIhhw4bBzMys2bbLli3DqlWrsHHjRnz++ed46aWX4Obmhv79+9/zHFVVVXj++edhbm6O1NRUdO7cGZ9++imioqKwZcsWeHp6IicnB7Nnz0bnzp0RFhbWZD/V1dVYuHAh3N3dUV1djbfffhuTJk3CmTNnYGT0x+TtW2+9hbVr16JPnz5466238NJLL+H8+fMwMTFBVlYWIiMj8e6772Ly5Mk4ePBgi3sjOzg44Ndff8XRo0fh4+Nzz7Zqv//+O8aNG4eVK1fC3NwcO3bsQFBQEAoKCtC7d2+p3XvvvYclS5YgOjoahw4dwhtvvIGnnnqq0cxma9I7mSwtLW2y/LfffgPQkESpp3c3b96MefPm6XsqyTvvvINbt27h2LFjGDlypEbdyZMn8f3339/3OR5G+r6Pzf2jVy9cvnz5cot3rVtbW0MQBFy9evWe7e7W0ufjzql/InoASvOB8gtA9yeAHk8ZOhrS083bdRjw9iGDnPun5c/B0rTltEG9c8js2bPx0Ucf4emnn4avry+mTp2KgQMHarR94YUXMGvWLADAihUr8PXXX2Pz5s2Ndj2502+//YYpU6bgySefRFxcHExNTaXj161bh8mTJwMAnJ2d8dNPP+Hjjz9uNpkMCQnReL1t2zb06NEDP/30k8ZM36JFizB+/HgADXseu7q64vz583jqqaewceNGPPfcc3jzzTcBNCw/O3HiBA4ePNjsNbzwwgs4dOgQfH194eDggGHDhmHUqFGYMWNGs78fBw0ahEGDBkmvV65ciYSEBCQnJ2vkBiNHjtSI5fjx43j//ffbNJnUe83k8ePHIYqiRtnNmzfx3XffwcLCAn379pXuLs7MzNS6X2Nj42ZH7AoLC9G9e/dGieSNGzeQnZ2t4xVonhPQfqSwLdwrBn3ex3tRTzUcPny4xbbe3t64du0azp07p9M5MjIyGpVduXIFhYWFePLJJzVGJY2MjAz63hO1O9VXtf8qOwtsHwd86A3sfqnh+/ZxDeXaHE+kh5CQEFy5cgXJycl47rnnoFAo8PTTTzfa9u3OqWL1659//vmefT/77LN44oknsGfPHimRLCsrwy+//IKZM2dKI6NWVlZYuXIlCgsLm+2rsLAQL7/8Mp544glYW1vD2dkZAHDx4kWNdncmweqldOpBk59//rnJ67gXY2NjbN++HZcuXcKaNWvw2GOP4Z133oGrqyt+/fXXJo+prq7G3//+dwwYMADdunWDlZUV8vPzG8Wqz3t6v/QemSwoKMBnn32GmTNnSmXvvfceysrKEBkZCVNTUwwdOhTe3t6Ii4vDhAkTMGXKFI0+6uvrkZGRIa2pAIDu3bsjLy8PNTU1MDc312gvk8lw9uxZ/Pjjj3B1dQXQkHwtWrQIZWVl+l4KundveKRYU3tkPij3ikGf9/FewsLCsHz5cqxbtw6TJk1qtBbl8uXL0hT4/PnzceDAAURGRiIxMRG2trYabUtKSlBRUdFoSuLrr79Gamqqxk04S5cuxe3btxv9hdi9e3eDvvdEbapWu2U8OnnvSe3bCkaAmTXwghzoPQK4eAL4ckFDUilqsdB/yRV9o9SOaee27b+DsehkjJ+WP2ewc+vC3Nwco0ePxujRo/H2229j1qxZiIqK0tgGriktTaWPHz8e8fHx+Omnn6T7AtQ3rXz66aeNtslTD9Y0JSgoCL169cKnn36Kxx57DPX19XBzc0Ntba1Gu06dOjWKT33OuwfWdPH4449j+vTpmD59OlauXIm+ffvio48+anJp2eLFi3Ho0CGsXbsWLi4usLCwQGhoaKNYm6LN8oT7oXcyOWbMGLz22mtISUnBU089hezsbBw6dAi9evXCu+++K7WLi4uDv78/pk6dig0bNmDw4MEwNzfHxYsXkZmZibKyMtTU1EjtAwIC8O233yIoKAh//vOfYWpqimeeeQbPPPMM/vrXv+Lw4cN45pln8OKLL8Lc3BwKhQKXL1+Gn5+f3s+1Vm8U/tZbbyE/Px9du3ZF165d8eqrr+r79rR6DLq+j/fSo0cP7Ny5E1OnTsXQoUMxYcIE9OvXD1evXkVWVhacnJyQmJgIAAgMDMSyZcuwYsUKuLi4IDAwEDKZDNeuXcP58+eRkZGBlStXNkomx48fj3HjxuGFF15Ar169kJ6ejszMTAwaNAiLFi3SaBsQEIA9e/YgNDQUnp6eMDY2xvjx45u8eYio3Xn3McOeX6wHgjYArpMaXrtOAkQR2Beh3fFtHX90Vdv238EIgqDVVPPDaMCAAdLvFrWTJ09ixowZGq+b2mXkTqtWrYKVlRVGjRoFhUKBAQMGwN7eHo8//jguXLiAadOmaRXPtWvX8PPPP+Pjjz+Wbho9duyYbheFhutS3yh753XoysbGBo6Ojs3eR5KRkYHw8HBMmtTwb/n333+HUqls1K6pWJ56qm2Xtuj9iRw+fDjeeustLF26FBs3boSpqSmmTp2KNWvWSDfhAJDuxFq/fj0SExPx2WefwdjYGI6OjvDx8UFoaKhGv8uWLUNFRQX279+PtLQ01NfXIyoqCs888wyef/557Nu3D++++y7+/e9/w9LSEgEBAUhISMDy5cv1fhMGDBiA7du3Y926dXj//fdx69YtyGSyB5pMthSDru9jSyZNmoSsrCzExsYiPT0dycnJsLOzg4eHh7SZutry5cvh4+ODTZs2ITU1FZWVlbC1tYWzszOio6Ob/IcbGhqKmTNn4p133kF8fDysra0xZ84cvPvuu422Ktq4cSMAIC0tDQkJCaivr4eDgwOTSaLW0nuE5mvZyKbbEbWCa9eu4YUXXkBkZCQGDhyILl264Ntvv8WaNWswceJEjbZ79+6Fl5cXnnnmGezatQunTp1qdveQO6mfwBYQEACFQoGnnnoK0dHRmD9/PqytrTF27FjcunUL3377LSoqKrBw4cJGfdjY2MDW1haffPIJHB0dcfHiRWmtoS7mz5+PESNGYM2aNQgODsbhw4fvuV4SaNhu78yZM5g0aRKefPJJ1NTUYOfOnfjxxx+xefPmJo9xcXHBf//7XwQFBUEQBCxbtqzJbYSOHz8uxfL1119j796997x5t1Xc1y6Vjwhws2ytqTct3759e6v3zZ8DtUu3fm/9ryhr3b7y/qsZU2689se2Rfx3flGz2uum5TU1NeKbb74pPv3002LXrl1FS0tLsV+/fuLSpUs1HqIBQPzggw/E0aNHi2ZmZqJMJhPj4uKk+qKiIhGAmJOTI4pi05uW//WvfxUdHR3FgoICURRFcdeuXaKHh4doamoq2tjYiD4+PuJ//3vX5/8OX3/9tdi/f3/RzMxMHDhwoKhQKEQAYkJCQpMxiKIoVlRUNHrIyLZt28SePXuKFhYWYlBQkLh27dp7bjienZ0tvvLKK6Kzs7NoZmYm2traij4+PmJycrLU5u5Ny4uKikR/f3/RwsJC7NWrl7hlyxbR19dXfP3116U2MplMjImJEV988UXR0tJStLe3Fzds2NBsHK31GRNE8T4m+x8Rd681uHnzZqP1nNRALpcjIiIC27dvb3FdjDa++eYbjTvQfH199V7OQNRh6HJjzJ4ZQFkBMO69hhHJ4uPAV4uBPz0FvKjFAwQ62+kfJ92XmpoaFBUVwdnZuUP+zhEEAQkJCQgODjZ0KI+s1vqMtc+FFw/Y3ftFdcTd+B9WTzzxhMb739JWRkSPBF0SvBc/B+Jnaq6RfMIfCNkGdLZt/jgiIi11yKxIqVQ22n6gKd26ddPqYehNPXWGHownnniC7z/R/ehsC8xI5D6TRNRmOuQ0t0KhgL+/f4vtZDJZk3dCERERGVpHn+Ymw+M09z34+fnd175PRERERKQdvZ+AQ0RERG2PgyPUVlrrs8VkkoiI6CGkfurKjRs3DBwJdVTqz9adT/jRR4ec5iYiImrvjI2N0a1bN+kZ0JaWlm3+WDx6NIiiiBs3bqC0tBTdunW75yMntdEhb8AhIiLqCERRRElJCSorKw0dCnVA3bp1g4ODw33/kcJkkoiI6CFXV1eH27dvGzoM6kA6dep03yOSakwmqUOrq6tr8tmlREREdG/arqXkmknq0H755RfU1tYaOgwiIqJ2p2/fvlq1YzJJHVptbS2MjY35CEwiIiIdqFQqrdvyNyx1eCYmJve97QERERE1jftMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHeuM8kERERUQuKrt3Epapa9OxqCmdbC0OH81BhMklERESPnIobt7VqV1WjwntHLiHn8u9SmefjVljs3xNdze+dRtlYPhoPzGAySURERA+Vm7fr2vwc4/+Vp1U7IwHoYt4JH7z8NIY42+B0UQWWJOTilV35qBfvfWzqqwNbIdKmWXQybrO+dcVk8i5yuRwRERHS6ylTpmD37t3S66ysLPzzn//E999/j/Lycvj6+kKhUBgg0o5PpVI1egyiKLbwL5eIiNq9UVt/MHQIknoReHeSO8YPdAQAjB/oCBEi5n2R0+KxbXkdJ+Z7tlnfumIy2YyJEyfCw8MDbm5uUllVVRWCgoJQW1uL6dOnw9bWFk5OTg8kHkEQOkzi+sUXX2DDhg348ccfYWpqiuHDh2P58uXw8vLSaGdkZISoqCgADUl+cXGxIcIlIqJH3BBnG43XQ527GyiShxOTyWYEBwcjPDxco+z06dMoKytDbGws3nzzTcME1s69++67eOutt9C7d2/MnTsXv//+O3bv3o2RI0fi0KFD8PPzk9oaGRkhOjoaAKBQKJhMEhE9ItpyelhNl1HD00UV0sgkAJwqKtfquAdxHQ8DJpM6uHLlCgDAwcHBwJG0T+fOnUNUVBT69u2LU6dOoWvXrgCA+fPnY+jQoZg1axby8/NhYsKPJRHRo+xBrAdMmeXWciMASw8osSwpFyJEDHXujlNF5Xg7KQ+ej1th5Vinex77MK1rbEvcZ1JLgiAgLCwMABAREQFBECAIgsa0c2lpKd544w24uLjAzMwMdnZ2CAkJQV5e40W+R44cQWRkJPr16wcrKytYWVnBy8sLn3zyiUY7hUIBQRAAAOnp6dJ5BUGAXC4HAERHRzeKRU0ul2u0BQClUglBEBAeHo78/HxMnjwZdnZ2EAQBSqVSapeUlIRRo0bBxsYG5ubmcHNzw9q1a1FXp9/C6O3bt0OlUuGtt96SEkkAcHV1xYwZM1BYWIi0tDS9+iYiItKFjWUnrb7eGecMF1tzzPsiB0PfScW8L3LgYmuOd8Y5t3jso4JDQFqKiorCmTNnkJSUJK2nBCCtmSwsLISfnx8uX76MMWPGIDg4GKWlpYiPj8ehQ4eQmpoKb29vqb/Vq1fj/PnzGDZsGCZNmoTKykocPHgQc+bMQUFBAdatWyf1HxUVhZiYGMhkMo2pd3UM+lKf39XVFWFhYSgvL4epqSkAYMmSJYiNjUXPnj0REhICa2trHD16FIsXL0ZWVhb27t2r8/nUye6YMWMa1T333HP46KOPkJ6e3mQ9ERGRIXSzMMGGYBfuM3kPTCa1FB0dDblcjqSkpCbXU86YMQMlJSU4dOgQRo8eLZUvXboUXl5emD17Nn744Y/1GVu3boWzs7NGHyqVCuPGjcPGjRvx+uuvo3fv3nByckJ0dDRiYmKk/24tx48fx7Jly7B8+XKN8q+//hqxsbEYO3Ys9u3bB0tLSwANd1K/9tpr+OijjxAfH4+QkBCdznfu3DlYWVk1uUygT58+UhsiIqKHjbOtBZPIZnCauxXk5OTgxIkTCAsL00gkAaBv376YPXs2cnNzNaa7704kAcDExARz585FXV0djhw50uZxOzg4YOnSpY3Kt2zZAgD4+OOPpUQSaJjqX7VqFQRBQFxcnM7nq6qq0pjevpO1tbXUhoiIiNoPjky2gpMnTwIASkpKmhw5zM/Pl76rtxq6fv061q5di8TERBQWFqK6ulrjGPXNPm1p0KBB0rT2nU6ePInOnTtj27ZtTR5nYWEhXRMRERE92phMtoLy8oYtAlJSUpCSktJsO3XCWFtbCz8/P2RnZ8PT01Pas9LExARKpRI7duzArVu32jxue3v7JsvLy8uhUqkQExPT7LF3J7/a6Nq1a7Mjj//73/+kNkRERNR+MJlsBeop2s2bN2PevHkttk9KSkJ2djZmzZqFTz/9VKNu9+7d2LFjh07nNzJqWK2gUqka1d1r2lh9l/jdrK2tIQgCrl69qlMcLenTpw8yMzNRUlLSaN2keq2keu0kERHRw8ak4jyMqy6irmtvqGxcDB3OQ4NrJluB+i7tzMxMrdoXFhYCACZMmNCoLiMjo8ljjIyMmt2Sx8amYWf+y5cvN6rLyWn5cU938/b2xrVr11r9ZhhfX18AwOHDhxvVHTp0SKMNERFRWzK6Wa71l3HFBdh+OQM99gbB9vBfGr5/OQPGFRe07qMj48hkKxg6dCi8vb0RFxeHCRMmYMqUKRr19fX1yMjIkBIlmUwGADh27BiCgoKkdunp6Y1GKtW6d++OS5cuNVmnfgzhzp07MX36dGmkMjMzE7t27dL5eubPn48DBw4gMjISiYmJsLW11agvKSlBRUUF+vfvr1O/ERERWLt2Ld555x1MnDhRmtL+8ccfsXPnTjz55JMICAjQOV4iIuq4hNs32qRfh89H6hCEEWBmDbwgB3qPAC6egNmXC2C/LwgQ67Xq4teI7/QLVEtiJ8uWG7URJpOtJC4uDv7+/pg6dSo2bNiAwYMHw9zcHBcvXkRmZibKyspQU1MDAAgKCoKTkxPWrFmDvLw8uLm5oaCgAPv370dwcDDi4+Mb9R8QEIA9e/YgNDQUnp6eMDY2xvjx4+Hu7o5hw4Zh+PDhSEtLw/Dhw+Hj44Pi4mIkJycjKCgICQkJOl1LYGAgli1bhhUrVsDFxQWBgYGQyWS4du0azp8/j4yMDKxcuVLnZLJv376Ijo7G0qVLMXDgQISGhqK6uhpxcXG4ffs2Pv30Uz79hoiINDhuH2zoEBoSxqANgOukhteukwBRBPZFaN1FW1/Hlf/7uU37vxf+5m4lzs7OyMnJwfr165GYmIjPPvsMxsbGcHR0hI+PD0JDQ6W2VlZWSEtLw+LFi3H06FEoFAq4urpi165dsLe3bzKZ3LhxIwAgLS0NCQkJqK+vh4ODA9zd3SEIApKTk7Fw4UKkpKQgNzcXgwYNQnJyMq5cuaJzMgkAy5cvh4+PDzZt2oTU1FRUVlbC1tYWzs7OiI6OxrRp0/R6n9566y04OTlhw4YN2Lp1K0xNTTFixAgsX74cQ4YM0atPIiKiNtd7hOZrmQ4jmx2cIIqiaOggHiZyuRwRERHYvn17o43JyXD8/PyQnp4OXT+uZ8+ehZmZGTp1enQea0VE1JG01TS3ziOFL8j/GJkEgLz/6jQy2d6muW/fvi0ty2sJRyabERERgYiICEyZMgW7d+82dDiPJJVKxSSQiOgR11ZrAUumH9e6rc03C2Ca8jcIotgwIll8HOJXi1DrOBQVz76vVR+GXNPY1phM3sXDwwNRUVHSa/Um4/TgGRkZafwsiIiIWku9RXet21aM3gibtEUwu2MksrbnCFQErEW9uU1bhNeucJqb9KZUKiGXy1ts161bNyxYsKDN42kKp7mJiKi1PEr7TOoyzc1kkvSmUCjg7+/fYjuZTAalUtn2ATWBySQREZHuuGaSHgg/Pz+db4ghIiKijoVPwCEiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK98W5u6vBUKpWhQyAiImpXdPndyWSSOjRTU1PU1tairq7O0KEQERF1SNy0nDq0uro61NfXGzoMIiKidkfbB34wmSQiIiIivfEGHCIiIiLSG5NJIiIiItIbk0kiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0huTSSIiIiLSG5+AQ0T0CDn323Uor92Ak60l+th3MXQ4RNQBMJkkInoElFfXYn5cDo6dvyqVPeNih00veaJ7Z1MDRkZE7R2fgENE1MHdqFVhpvxbnP3tOpZPdMMQZxucLqrA20l5GPCYNT6f6W3oEImoHeOaybvI5XIIgiB9TZ06VaM+KysLAQEBsLW1hSAI8PPzM0ygjwCVSqXxsxAEwdAhEbVLA94+hMwL17B8ohvGD3REjy7mGD/QETETXZFx7irO/Xbd0CESUTvGae5mTJw4ER4eHnBzc5PKqqqqEBQUhNraWkyfPh22trZwcnJ6IPEIggBfX18oFIoHcr62cOPGDWzduhXfffcdsrOzcfbsWYiiiKKioibfRyMjI0RFRQFoSPKLi4sfcMREHcsQZxuN10OduwMAlNducP0kEemNyWQzgoODER4erlF2+vRplJWVITY2Fm+++aZhAmvHSktLsWjRIgCATCaDjY0NysvLm21vZGSE6OhoAIBCoWAySaSn5HkjMWHLcZwuqsD4gY5S+amihn9/TraWhgqNiDoATnPr4MqVKwAABwcHA0fSPtnZ2eHw4cO4du0alEolhgwZYuiQiB4JA3t2wzMudng7KQ/7f7iC0us12P/DFUQl/Yg/97HjqCQR3Rcmk1oSBAFhYWEAgIiICGkN353TzqWlpXjjjTfg4uICMzMz2NnZISQkBHl5eY36O3LkCCIjI9GvXz9YWVnBysoKXl5e+OSTTzTaKRQKaa1genq6xvpBuVwOAIiOjm4Ui5p6Dai6LQAolUoIgoDw8HDk5+dj8uTJsLOzgyAIUCqVUrukpCSMGjUKNjY2MDc3h5ubG9auXYu6ujq93kMrKyuMHj0a3bt31+t4ItLfppc8MeAxa8z7IgdD30nFvC9yMOAxa2yc6mno0IioneM0t5aioqJw5swZJCUlSespAUhr/QoLC+Hn54fLly9jzJgxCA4ORmlpKeLj43Ho0CGkpqbC2/uPOyZXr16N8+fPY9iwYZg0aRIqKytx8OBBzJkzBwUFBVi3bp3Uf1RUFGJiYiCTyTSm3tUx6Et9fldXV4SFhaG8vBympg1bhCxZsgSxsbHo2bMnQkJCYG1tjaNHj2Lx4sXIysrC3r177+vcRPRgde9sis9nenOfSSJqfSJp2L59uwhA3L59u051I0aMEE1MTMTDhw9rlBcUFIhdunQR3d3dNcovXLjQqI/bt2+Lo0ePFo2NjcXi4mKNOgCir69vkzFHRUWJAMQjR45oFXNRUZEIQAQgLlu2rNExhw8fFgGIY8eOFaurq6Xy+vp6ce7cuSIAcd++fU3GoovnnntOBCAWFRW12NbX11fkx5WIiOjhw2nuVpCTk4MTJ04gLCwMo0eP1qjr27cvZs+ejdzcXI3pbmdn50b9mJiYYO7cuairq8ORI0faPG4HBwcsXbq0UfmWLVsAAB9//DEsLf9YmC8IAlatWgVBEBAXF9fm8REREdHDj9PcreDkyZMAgJKSEunu4zvl5+dL39VbDV2/fh1r165FYmIiCgsLUV1drXGM+maftjRo0CBpWvtOJ0+eROfOnbFt27Ymj7OwsJCuiYiIiB5tTCZbgXp7m5SUFKSkpDTbTp0w1tbWws/PD9nZ2fD09JT2rDQxMYFSqcSOHTtw69atNo/b3t6+yfLy8nKoVCrExMQ0e+zdyS8RdSCl+UD5BaD7E0CPpwwdDRE95JhMtgJra2sAwObNmzFv3rwW2yclJSE7OxuzZs3Cp59+qlG3e/du7NixQ6fzGxk1rFZQqVSN6qqqqpo9rrknylhbW0MQBFy9erXJeiJqR6p1+Hd8oxzYvwAoPv5HmWwk8PwGwFKLXRg62+kaHRF1AEwmW4H6Lu3MzEytksnCwkIAwIQJExrVZWRkNHmMkZFRs1vy2Ng0PNXi8uXLjepycnJajOdu3t7eOHDgAM6dO4c+ffrofDwR6aG2jUb733tS+7aCEWBmDbwgB3qPAC6eAL5cAHzoDYj1LR+/pI2W55h2bpt+iahVMJlsBUOHDoW3tzfi4uIwYcIETJkyRaO+vr4eGRkZ8PX1BdDw9BcAOHbsGIKCgqR26enpjUYq1bp3745Lly41Wefl5QUA2LlzJ6ZPny6NVGZmZmLXrl06X8/8+fNx4MABREZGIjExEba2thr1JSUlqKioQP/+/XXum4ia8e5jho6gIWEM2gC4Tmp47ToJEEVgX4R2x7fVNUQ3P8NCRIbHZLKVxMXFwd/fH1OnTsWGDRswePBgmJub4+LFi8jMzERZWRlqamoAAEFBQXBycsKaNWuQl5cHNzc3FBQUYP/+/QgODkZ8fHyj/gMCArBnzx6EhobC09MTxsbGGD9+PNzd3TFs2DAMHz4caWlpGD58OHx8fFBcXIzk5GQEBQUhISFBp2sJDAzEsmXLsGLFCri4uCAwMBAymQzXrl3D+fPnkZGRgZUrV+qVTC5atEiaPs/NzZXKrKysAABvvvkmnnqKa7SIDKb3CM3XspGGiYOI2g0mk63E2dkZOTk5WL9+PRITE/HZZ5/B2NgYjo6O8PHxQWhoqNTWysoKaWlpWLx4MY4ePQqFQgFXV1fs2rUL9vb2TSaTGzduBACkpaUhISEB9fX1cHBwgLu7OwRBQHJyMhYuXIiUlBTk5uZi0KBBSE5OxpUrV3ROJgFg+fLl8PHxwaZNm5CamorKykrY2trC2dkZ0dHRmDZtml7v0759+xo9Y/vO6w0PD2cySY+mtpoi1nW08OKJP0YmAc31ky1pq2sgooeaIIqiaOggHiZyuRwRERHYvn27xtNmyLD8/PyQnp4OflyJdKTLDTh7ZgBlBcC49xpGJIuPA18tBv70FPCiFjcG8gYcokcSk8m7qJNJtSlTpmD37t0GjOjRpVKp0KlTJ40yflyJ2lD1NSB+JnDhjocmPOEPhGwDOts2fxwRPdI4zX0XDw8PREVFSa/Vm4zTg2dkZKTxsyCiNtbZFpiRyH0miUgnHJkkvSmVSsjl8hbbdevWDQsWLGjzeIiIiOjBYzJJelMoFPD392+xnUwmg1KpbPuAiIiI6IFjMklEREREejMydABERERE1H4xmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivXHTcurQ6urqUF9fb+gwiIiI2p27n0LXHCaT1KH98ssvqK2tNXQYRERE7U7fvn21asdkkjq02tpaGBsbw8SEH3UiIiJtqVQqrdvyNyx1eCYmJloP1RMREZFueAMOEREREemNySQRERER6Y3JJBERERHpjckkEREREemNySQRERER6Y3JJBERERHpjckkEREREemN+0wSdRBF127iUlUtenY1hbOthaHDISKiRwSTSaKHUMWN21q3rapR4b0jl5Bz+XepzPNxKyz274mu5vf+J25jyc3ciYjo/jCZJGoFN2/XtWp/4/+Vp3VbIwHoYt4JH7z8NIY42+B0UQWWJOTilV35qBfvfWzqqwPvM9KmWXQybpN+iYjo4cNk8i5yuRwRERHS6ylTpmD37t3S66ysLPzzn//E999/j/Lycvj6+kKhUBgg0o5PpVI1egyiKLaQHRnIqK0/GOzc9SLw7iR3jB/oCAAYP9ARIkTM+yKnxWPbKu4T8z3bpF8iInr4MJlsxsSJE+Hh4QE3NzeprKqqCkFBQaitrcX06dNha2sLJyenBxKPIAjtPnE9c+YM4uPj8fXXX+PChQuoqqrC448/jsDAQLz11lt4/PHHNdobGRkhKioKQEOSX1xcbIiw24UhzjYar4c6dzdQJERE9KhhMtmM4OBghIeHa5SdPn0aZWVliI2NxZtvvmmYwNqxuXPn4tSpUxgyZAimTp0KMzMzZGVlYevWrdi7dy8yMjLw1FNPSe2NjIwQHR0NAFAoFA91Mtna08W6jhieLqqQRiYB4FRRuVbHtdU0NxERPTqYTOrgypUrAAAHBwcDR9I+vfLKK9i1axeefPJJjfLVq1fjzTffxN/+9jekpKQYKLr709prBFNmubXc6P9bekCJZUm5ECFiqHN3nCoqx9tJefB83Aorxzrd81iubSQiovvFfSa1JAgCwsLCAAAREREQBAGCIGhMO5eWluKNN96Ai4sLzMzMYGdnh5CQEOTlNb6Z4siRI4iMjES/fv1gZWUFKysreHl54ZNPPtFop1AoIAgCACA9PV06ryAIkMvlAIDo6OhGsajJ5XKNtgCgVCohCALCw8ORn5+PyZMnw87ODoIgQKlUSu2SkpIwatQo2NjYwNzcHG5ubli7di3q6vS72WTevHmNEkkAWLRoESwtLZGenq5Xvx2RjWUnrb/eGecMF1tzzPsiB0PfScW8L3LgYmuOd8Y5t3gsERHR/eLIpJaioqJw5swZJCUlSespAUhrJgsLC+Hn54fLly9jzJgxCA4ORmlpKeLj43Ho0CGkpqbC29tb6m/16tU4f/48hg0bhkmTJqGyshIHDx7EnDlzUFBQgHXr1kn9R0VFISYmBjKZTGPqXR2DvtTnd3V1RVhYGMrLy2FqagoAWLJkCWJjY9GzZ0+EhITA2toaR48exeLFi5GVlYW9e/fe17nvJAgCjI2NYWTEv2300c3CBBuCXbjPJBERGQSTSS1FR0dDLpcjKSmpyfWUM2bMQElJCQ4dOoTRo0dL5UuXLoWXlxdmz56NH374Yx3c1q1b4ezsrNGHSqXCuHHjsHHjRrz++uvo3bs3nJycEB0djZiYGOm/W8vx48exbNkyLF++XKP866+/RmxsLMaOHYt9+/bB0tISQMOd1K+99ho++ugjxMfHIyQkpFXi2LdvH65fv44XXnihVfp7VDnbWjCJJCKiB45DQa0gJycHJ06cQFhYmEYiCQB9+/bF7NmzkZubqzHdfXciCQAmJiaYO3cu6urqcOTIkTaP28HBAUuXLm1UvmXLFgDAxx9/LCWSQMMI4qpVqyAIAuLi4lolhl9++QXz58+HhYUFVqxY0Sp9EhER0YPDkclWcPLkSQBASUlJkyOH+fn50nf1VkPXr1/H2rVrkZiYiMLCQlRXV2sco77Zpy0NGjRImta+08mTJ9G5c2ds27atyeMsLCyka7of5eXlGDduHEpLS7Fz507069fvvvskIiKiB4vJZCsoL2/YhiUlJeWedyOrE8ba2lr4+fkhOzsbnp6e0p6VJiYmUCqV2LFjB27dutXmcdvb2zdZXl5eDpVKhZiYmGaPvTv51VVFRQWeffZZ/Pjjj9i6dSteeeWV++rvUWVScR7GVRdR17U3VDYuhg6HiIgeQUwmW4G1tTUAYPPmzZg3b16L7ZOSkpCdnY1Zs2bh008/1ajbvXs3duzYodP51TeuqFSqRnVVVVXNHqe+S/xu1tbWEAQBV69e1SkObZWXl+PZZ59FTk4OPvjgA8yZM6dNztMeGd3Ubn9IoaYS3Y5Fw+zX01LZLcchqHwmGqJ5txaPr7fgpuZERNQ6mEy2AvVd2pmZmVolk4WFhQCACRMmNKrLyMho8hgjI6Nmt+SxsWl4+snly5cb1eXktPxIvbt5e3vjwIEDOHfuHPr06aPz8fdyZyK5efNmvPbaa63a/4Mg3L7RZn07fD5SyyCMADNr4AU50HsEcPEEzL5cAPt9QYBY3+Lhv0Z8d3+BNkHsZNlyIyIi6nCYTLaCoUOHwtvbG3FxcZgwYQKmTJmiUV9fX4+MjAz4+voCAGQyGQDg2LFjCAoKktqlp6c3GqlU6969Oy5dutRknZeXFwBg586dmD59ujRSmZmZiV27dul8PfPnz8eBAwcQGRmJxMRE2NraatSXlJSgoqIC/fv316nf8vJyjBo1CmfOnMHGjRu1SrwfRo7bBxs6hIaEMWgD4Dqp4bXrJEAUgX0R9zxMrS2u4cr//dzqfRIR0cOPyWQriYuLg7+/P6ZOnYoNGzZg8ODBMDc3x8WLF5GZmYmysjLU1NQAAIKCguDk5IQ1a9YgLy8Pbm5uKCgowP79+xEcHIz4+PhG/QcEBGDPnj0IDQ2Fp6cnjI2NMX78eLi7u2PYsGEYPnw40tLSMHz4cPj4+KC4uBjJyckICgpCQkKCTtcSGBiIZcuWYcWKFXBxcUFgYCBkMhmuXbuG8+fPIyMjAytXrtQ5mZw8eTLOnDmDp556CuXl5U3erLRgwQJ069ZNp34fWb1HaL6WaTmqSURE1IqYTLYSZ2dn5OTkYP369UhMTMRnn30GY2NjODo6wsfHB6GhoVJbKysrpKWlYfHixTh69CgUCgVcXV2xa9cu2NvbN5lMbty4EQCQlpaGhIQE1NfXw8HBAe7u7hAEAcnJyVi4cCFSUlKQm5uLQYMGITk5GVeuXNE5mQSA5cuXw8fHB5s2bUJqaioqKytha2sLZ2dnREdHY9q0aTr3qX66Tn5+frM394SHhz/0yWRbTBGr6TRiePHEHyOTAFB8XOtD2/IaiIjo0SKIoigaOoiHiVwuR0REBLZv395oY3IyHD8/P6Snp0PXj+vZs2dhZmaGTp3ax6MDtb0Bx+abBTCtugBh3NqGEcni4xC/WoTark+i4tn3WzyeN+AQEdG93L59W1qW1xImk3dRJ5NqU6ZMwe7duw0Y0aNLpVI1SgI7ejKpLaOaCtikLYLZpRNS2a2eI1ARsBb15jYGjIyIiDoCXZJJTnPfxcPDA1FRUdJr9Sbj9OAZGRlp/CzoD/XmNrg2bhv3mSQiIoPjyCTpTalUQi6Xt9iuW7duWLBgQZvH05SOOjJJRETUljjNTQ+EQqGAv79/i+1kMpl0882DxmSSiIhId5zmpgfCz89P5zWMRERE1LEYGToAIiIiImq/mEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd54Nzd1eCqVytAhEBERtSu6/O5kMkkdmqmpKWpra1FXV2foUIiIiDokblpOHVpdXR3q6+sNHQYREVG7o+0DP5hMEhEREZHeeAMOEREREemNySQRERER6Y3JJBERERHpjckkEREREemNySQRERER6Y3JJBERERHpjckkEREREemNT8AhIiLqgM79dh3KazfgZGuJPvZdDB0OdWBMJomIiNqBa7/f0qpdxY1avJWQh6yicqnM27k73pnkBhtL02aPs7Uyu+8Y6dHEJ+AQERG1shu1qlbvc8Dbh7RqZyQAXcw74d1J7hjibIPTRRVYkpCL6zW3UX+P3/g/LX+ulSJtzNKUY1cdGZPJu8jlckREREivp0yZgt27d0uvs7Ky8M9//hPff/89ysvL4evrC4VCYYBIOz6VStXouaD8uBJRe+D0ZopBz//By09j/EBH6fX+H65g3hc5BotHuWq8wc5NbY9/KjRj4sSJ8PDwgJubm1RWVVWFoKAg1NbWYvr06bC1tYWTk9MDiUcQhHafuH7//ffYsGEDvvvuO1y+fBnV1dV4/PHHMXjwYPz973+Hl5eXRnsjIyNERUUBaEjyi4uLDRE2EVG7M8TZRuP1UOfuBoqEHgVMJpsRHByM8PBwjbLTp0+jrKwMsbGxePPNNw0TWDt2+vRpfPXVVxg+fDh8fX3RuXNnXLhwAV9++SX27duHnTt34pVXXpHaGxkZITo6GgCgUCiYTBJRu9EWU8baTnMDwOmiCo2RyVN3rJ9sTltOc1PHxmRSB1euXAEAODg4GDiS9umVV17BrFmzGpX/+OOP8PLywt/+9jdMmzYNgiAYIDoiotbTFmsEv1v6rFbtXtuVjWVJeRAhYqhzd5wqKsfbST/C27k7Ppz2dLPHcV0j6Yv7TGpJEASEhYUBACIiIiAIAgRB0Jh2Li0txRtvvAEXFxeYmZnBzs4OISEhyMvLa9TfkSNHEBkZiX79+sHKygpWVlbw8vLCJ598otFOoVBIyVV6erp0XkEQIJfLAQDR0dGNYlGTy+UabQFAqVRCEASEh4cjPz8fkydPhp2dHQRBgFKplNolJSVh1KhRsLGxgbm5Odzc3LB27VrU1dXp9R6am5s3We7q6or+/fujtLQU//vf//Tqm4ioo7O1MtPqa+srg+H6mDXmfZGDoe+kYt4XOXB9zBpbXxl8z+OI9MU/Q7QUFRWFM2fOICkpSVpPCUBaM1lYWAg/Pz9cvnwZY8aMQXBwMEpLSxEfH49Dhw4hNTUV3t7eUn+rV6/G+fPnMWzYMEyaNAmVlZU4ePAg5syZg4KCAqxbt07qPyoqCjExMZDJZBpT7+oY9KU+v6urK8LCwlBeXg5T04ZtI5YsWYLY2Fj07NkTISEhsLa2xtGjR7F48WJkZWVh796993XuOxUWFqKgoAC9evVC165dW61fIqJHUffOpvh8pjf3maQHRyQN27dvFwGI27dv16luxIgRoomJiXj48GGN8oKCArFLly6iu7u7RvmFCxca9XH79m1x9OjRorGxsVhcXKxRB0D09fVtMuaoqCgRgHjkyBGtYi4qKhIBiADEZcuWNTrm8OHDIgBx7NixYnV1tVReX18vzp07VwQg7tu3r8lYtJGTkyNGRUWJS5YsEadNmyZ26dJFtLS0FFNSUpo9xtfXV+THlYiI6OHDae5WkJOTgxMnTiAsLAyjR4/WqOvbty9mz56N3NxcjeluZ2fnRv2YmJhg7ty5qKurw5EjR9o8bgcHByxdurRR+ZYtWwAAH3/8MSwtLaVyQRCwatUqCIKAuLg4vc975swZxMTE4N1338WuXbtgaWmJhIQEjBs3Tu8+iYiIyDA4zd0KTp48CQAoKSmR7j6+U35+vvRdvdXQ9evXsXbtWiQmJqKwsBDV1dUax6hv9mlLgwYNkqa173Ty5El07twZ27Zta/I4CwsL6Zr0ER4ejvDwcNTU1ODcuXNYt24dxo4di9WrV2PRokV690tEREQPHpPJVlBe3rDlQkpKClJSmt+oVp0w1tbWws/PD9nZ2fD09JT2rDQxMYFSqcSOHTtw65Z2j826H/b29k2Wl5eXQ6VSISYmptlj705+9WFubg53d3fI5XKUlZXhH//4BwIDAzX29iQiIh2U5gPlF4DuTwA9njJ0NPSIYDLZCqytrQEAmzdvxrx581psn5SUhOzsbMyaNQuffvqpRt3u3buxY8cOnc5vZNSwWkGlavz4rqqqqmaPa24LHmtrawiCgKtXr+oUx/0YM2YMvvrqK2RkZDCZJCJSq9by/8M3yoH9C4Di43+UyUYCz28ALLXYsLyznT7REQFgMtkq1HdpZ2ZmapVMFhYWAgAmTJjQqC4jI6PJY4yMjJrdksfGpuFJB5cvX25Ul5Oj++OzvL29ceDAAZw7dw59+vTR+Xh9qKf1TUz4kSSidqD2/mdntPLek9q1E4wAM2vgBTnQewRw8QTw5QLgQ29ArG/5+CVttLTKtHPb9EsPFf7mbgVDhw6Ft7c34uLiMGHCBEyZMkWjvr6+HhkZGfD19QUAyGQyAMCxY8cQFBQktUtPT280UqnWvXt3XLp0qck69WMId+7cienTp0sjlZmZmdi1a5fO1zN//nwcOHAAkZGRSExMhK2trUZ9SUkJKioq0L9/f536PX78OLy9vRsljGfOnMFHH30EExOTRjcwERE9lN59zNARaBLrgaANgOukhteukwBRBPZFaHd8W11PdPOzY9RxMJlsJXFxcfD398fUqVOxYcMGDB48GObm5rh48SIyMzNRVlaGmpoaAEBQUBCcnJywZs0a5OXlwc3NDQUFBdi/fz+Cg4MRHx/fqP+AgADs2bMHoaGh8PT0hLGxMcaPHw93d3cMGzYMw4cPR1paGoYPHw4fHx8UFxcjOTkZQUFBSEhI0OlaAgMDsWzZMqxYsQIuLi4IDAyETCbDtWvXcP78eWRkZGDlypU6J5N/+ctfUFZWhpEjR6J3795QqVQoKCjA4cOHIYoi1q9f/8CedU5E1OH0HqH5WjbSMHHQI4fJZCtxdnZGTk4O1q9fj8TERHz22WcwNjaGo6MjfHx8EBoaKrW1srJCWloaFi9ejKNHj0KhUMDV1RW7du2Cvb19k8nkxo0bAQBpaWlISEhAfX09HBwc4O7uDkEQkJycjIULFyIlJQW5ubkYNGgQkpOTceXKFZ2TSQBYvnw5fHx8sGnTJqSmpqKyshK2trZwdnZGdHQ0pk2bpnOff/vb3/Df//4Xp06dwv79+1FXVwdHR0dMnToV8+bNw/Dhw3Xuk4jIINpqWvhuuowYXjzxx8gkoLl+siUP6nqoQxJEURQNHcTDRC6XIyIiAtu3b9d42gwZlp+fH9LT08GPKxE9UrS9AWfPDKCsABj3XsOIZPFx4KvFwJ+eAl7U4qZO3oBD94HJ5F3UyaTalClTsHv3bgNG9OhSqVTo1KmTRhk/rkRETai+BsTPBC7c8cCLJ/yBkG1AZ9vmjyNqBZzmvouHhweioqKk19ymxnCMjIw0fhZERNSMzrbAjETuM0kGwZFJ0ptSqYRcLm+xXbdu3bBgwYI2j4eIiIgePCaTpDeFQgF/f/8W28lkMiiVyrYPiIiIiB44JpNEREREpDcjQwdARERERO0Xk0kiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0hs3LacOra6uDvX19YYOg4iIqN25+yl0zWEySR3aL7/8gtraWkOHQURE1O707dtXq3ZMJqlDq62thbGxMUxM+FEnIiLSlkql0rotf8NSh2diYqL1UD0RERHphjfgEBEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHeuM8kEdEjpOjaTVyqqkXPrqZwtrUwdDhE1AEwmSQiegRU3lQh6qASp3+5LpUN6dUFMYFO6GbBXwVEpD9OcxMRdVA3b9dJX8sOFKGwvAYfvPw0Tr01Ch+8/DQKy2uw7ECRocMkonaOyeRd5HI5BEGQvqZOnapRn5WVhYCAANja2kIQBPj5+Rkm0EeASqXS+FkIgmDokIjalVFbf5C+vrv0O1ZMdMf4gY7o0cUc4wc6YvlEN3x36XcUXbtp6FCJqB3j3EYzJk6cCA8PD7i5uUllVVVVCAoKQm1tLaZPnw5bW1s4OTk9kHgEQYCvry8UCsUDOd+DMn78eHz11VcwMzNDTU2NRp2RkRGioqIANCT5xcXFhgiRqMMY4myj8Xqoc3cAwKWqWq6fJCK9MZlsRnBwMMLDwzXKTp8+jbKyMsTGxuLNN980TGAdyGeffYaDBw/C3Nwcoig2qjcyMkJ0dDQAQKFQMJkk0lHqqwMBAMryGsz8z1mcLqrA+IGOUv2ponIAQM+upgaJj4g6Bk5z6+DKlSsAAAcHBwNH0v5dunQJCxcuxIIFC2Bvb2/ocIg6JItOxrDoZIz+9p0xpFcXLEvKxf4frqD0eg32/3AFbyflYWjvLhyVJKL7wmRSS4IgICwsDAAQEREhreG7c9q5tLQUb7zxBlxcXGBmZgY7OzuEhIQgLy+vUX9HjhxBZGQk+vXrBysrK1hZWcHLywuffPKJRjuFQiGtFUxPT9dYPyiXywEA0dHRjWJRU68BVbcFAKVSCUEQEB4ejvz8fEyePBl2dnYQBAFKpVJql5SUhFGjRsHGxgbm5uZwc3PD2rVrUVdXp9+beIeZM2fiT3/6E1auXHnffRFRy2ICneBia455X+Rg6DupmPdFDlxszRH9nJOhQyOido7T3FqKiorCmTNnkJSUJK2nBCCtmSwsLISfnx8uX76MMWPGIDg4GKWlpYiPj8ehQ4eQmpoKb29vqb/Vq1fj/PnzGDZsGCZNmoTKykocPHgQc+bMQUFBAdatWyf1HxUVhZiYGMhkMo2pd3UM+lKf39XVFWFhYSgvL4epacN015IlSxAbG4uePXsiJCQE1tbWOHr0KBYvXoysrCzs3btX7/N+8skn+Prrr3HkyBFYWHBEhOhB6GZhgg3BLtxnkohaHZNJLUVHR0MulyMpKanJ9ZQzZsxASUkJDh06hNGjR0vlS5cuhZeXF2bPno0ffvhBKt+6dSucnZ01+lCpVBg3bhw2btyI119/Hb1794aTkxOio6MRExMj/XdrOX78OJYtW4bly5drlH/99deIjY3F2LFjsW/fPlhaWgIARFHEa6+9ho8++gjx8fEICQnR+ZzFxcVYtGgR5s6dC19f31a5DiLSnrOtBZNIImpVnOZuBTk5OThx4gTCwsI0EkkA6Nu3L2bPno3c3FyN6e67E0kAMDExwdy5c1FXV4cjR460edwODg5YunRpo/ItW7YAAD7++GMpkQQapvpXrVoFQRAQFxen8/lEUcTMmTNhY2OD1atX6x84ERERPTQ4MtkKTp48CQAoKSlpcuQwPz9f+q7eauj69etYu3YtEhMTUVhYiOrqao1j1Df7tKVBgwZJ09p3OnnyJDp37oxt27Y1eZyFhYV0TbrYunUrUlNTceDAAXTp0kXn44mIiOjhw2SyFZSXN2yvkZKSgpSUlGbbqRPG2tpa+Pn5ITs7G56entKelSYmJlAqldixYwdu3brV5nE3dxd1eXk5VCoVYmJimj327uS3JZcvX8Y//vEPhIeHIzAwUKdjiejhYVJxHsZVF1HXtTdUNi6GDoeIHgJMJluBtbU1AGDz5s2YN29ei+2TkpKQnZ2NWbNm4dNPP9Wo2717N3bs2KHT+Y2MGlYrqFSqRnVVVVXNHtfcE2Wsra0hCAKuXr2qUxz3cu7cOfz++++Qy+Uad5Y3FU9FRQW6devWaucmouYZ3SzXqp1QU4lux6Jh9utpqeyW4xBUPhMN0bybVn3UW3TXJ0QiesgxmWwF6ru0MzMztUomCwsLAQATJkxoVJeRkdHkMUZGRs1uyWNj0/BUi8uXLzeqy8nJaTGeu3l7e+PAgQM4d+4c+vTpo/PxTXF0dMTMmTObrPvPf/6DmzdvSjc1mZmZtco5iToC4faNNu3f4fORWgZiBJhZAy/Igd4jgIsnYPblAtjvCwLEeq26+DXiO/0DbYHYybLlRkTUJphMtoKhQ4fC29sbcXFxmDBhAqZMmaJRX19fj4yMDOnuZZlMBgA4duwYgoKCpHbp6emNRirVunfvjkuXLjVZ5+XlBQDYuXMnpk+fLo1UZmZmYteuXTpfz/z583HgwAFERkYiMTERtra2GvUlJSWoqKhA//79te6zX79++Ne//tVk3TfffIOSkpJm64keZY7bBxs6hAZiPRC0AXCd1PDadRIgisC+CK27aMtrufJ/P7dZ30R0b0wmW0lcXBz8/f0xdepUbNiwAYMHD4a5uTkuXryIzMxMlJWVSc+eDgoKgpOTE9asWYO8vDy4ubmhoKAA+/fvR3BwMOLj4xv1HxAQgD179iA0NBSenp4wNjbG+PHj4e7ujmHDhmH48OFIS0vD8OHD4ePjg+LiYiQnJyMoKAgJCQk6XUtgYCCWLVuGFStWwMXFBYGBgZDJZLh27RrOnz+PjIwMrFy5Uqdkkog6gN4jNF/LtBzVJKIOjclkK3F2dkZOTg7Wr1+PxMREfPbZZzA2NoajoyN8fHwQGhoqtbWyskJaWhoWL16Mo0ePQqFQwNXVFbt27YK9vX2TyeTGjRsBAGlpaUhISEB9fT0cHBzg7u4OQRCQnJyMhQsXIiUlBbm5uRg0aBCSk5Nx5coVnZNJAFi+fDl8fHywadMmpKamorKyEra2tnB2dkZ0dDSmTZum/5tFRFpry6lhQMfRwosn/hiZBIDi4zqdq62vhYgMQxBFUTR0EA8TuVyOiIgIbN++vdHG5GQ4fn5+SE9Ph64f17Nnz8LMzAydOnVqo8iI2jdtb8Cx+WYBTKsuQBi3tmFEsvg4xK8Wobbrk6h49n2t+uANOETtx+3bt6VleS1hMnkXdTKpNmXKFOzevduAET26VCpVoySQySSRYRjVVMAmbRHMLp2Qym71HIGKgLWoN7cxYGRE1BZ0SSY5zX0XDw8PREVFSa/Vm4zTg2dkZKTxsyAiw6k3t8G1cdu4zyQRNcKRSdKbUqlsds/IO3Xr1g0LFixo83iawpFJIiIi3XGamx4IhUIBf3//FtvJZDIolcq2D6gJTCaJiIh0x2lueiD8/Px0XsNIREREHYuRoQMgIiIiovaLySQRERER6Y3JJBERERHpjckkEREREemNySQRERER6Y13c1OHp1KpDB0CERFRu6LL704mk9ShmZqaora2FnV1dYYOhYiIqEPipuXUodXV1aG+vt7QYRAREbU72j7wg8kkEREREemNN+AQERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd74BBwiokfIud+uQ3ntBpxsLdHHvouhwyGiDoDJJBHRI6C8uhbz43Jw7PxVqewZFztseskT3TubGjAyImrvOM19F7lcDkEQpK+pU6dq1GdlZSEgIAC2trYQBAF+fn6GCfQRoFKpNH4WgiAYOiSidusvu7Lx86//wwcvP41Tb43CBy8/jZ9//R9e351j6NCIqJ3jyGQzJk6cCA8PD7i5uUllVVVVCAoKQm1tLaZPnw5bW1s4OTk9kHgEQYCvry8UCsUDOV9bUCqVcHZ2brY+Li5OI3k3MjJCVFQUgIYkv7i4uM1jJOqIzv12HZkXruGDl5/G+IGOAIDxAx0hQsS8L3Jw7rfrnPImIr0xmWxGcHAwwsPDNcpOnz6NsrIyxMbG4s033zRMYB3AoEGDEBwc3Kj8zsQdaEgmo6OjAQAKhYLJJJGelNduAACGONtolA917i7VM5kkIn0xmdTBlStXAAAODg4GjqR98/DwkJJEImp7TraWAIDTRRXSyCQAnCoq16gnItIH10xqSRAEhIWFAQAiIiKkNXx3TjuXlpbijTfegIuLC8zMzGBnZ4eQkBDk5eU16u/IkSOIjIxEv379YGVlBSsrK3h5eeGTTz7RaKdQKKS1gunp6RrrB+VyOQAgOjq6USxq6jWg6rZAw3SzIAgIDw9Hfn4+Jk+eDDs7OwiCAKVSKbVLSkrCqFGjYGNjA3Nzc7i5uWHt2rWoq6vT700kIoPoY98Fz7jY4e2kPOz/4QpKr9dg/w9XEJX0I/7cx46jkkR0XzgyqaWoqCicOXMGSUlJ0npKANKaycLCQvj5+eHy5csYM2YMgoODUVpaivj4eBw6dAipqanw9vaW+lu9ejXOnz+PYcOGYdKkSaisrMTBgwcxZ84cFBQUYN26dVL/UVFRiImJgUwm05h6V8egL/X5XV1dERYWhvLycpiaNtzVuWTJEsTGxqJnz54ICQmBtbU1jh49isWLFyMrKwt79+7V+7xXrlzB1q1bUVlZicceewyjRo1Cz5497+taiOjeNr3kidd352DeF3/ccPPnPnbYONXTgFERUYcgkobt27eLAMTt27frVDdixAjRxMREPHz4sEZ5QUGB2KVLF9Hd3V2j/MKFC436uH37tjh69GjR2NhYLC4u1qgDIPr6+jYZc1RUlAhAPHLkiFYxFxUViQBEAOKyZcsaHXP48GERgDh27FixurpaKq+vrxfnzp0rAhD37dvXZCz3cud57/wyMTERFy5cKNbV1TV7rK+vr8iPK9H9O1vyP/HwjyXi2ZL/GToUIuogOM3dCnJycnDixAmEhYVh9OjRGnV9+/bF7NmzkZubqzHd3dRdzSYmJpg7dy7q6upw5MiRNo/bwcEBS5cubVS+ZcsWAMDHH38MS8s/1lIJgoBVq1ZBEATExcXpfD5LS0tphPd///sfSktLkZycjD59+mD9+vV466239L8YItJKH/suGD3AnlPbRNRqOM3dCk6ePAkAKCkpafLGkvz8fOm7+o7l69evY+3atUhMTERhYSGqq6s1jlHf7NOWBg0aJE1r3+nkyZPo3Lkztm3b1uRxFhYW0jXpokePHhrvT5cuXRAUFIQhQ4bAzc0N69evx9///nfY2Ng03wkRERE9VJhMtoLy8oY7IlNSUpCSktJsO3XCWFtbCz8/P2RnZ8PT01Pas9LExARKpRI7duzArVu32jxue3v7JsvLy8uhUqkQExPT7LF3J7/3w8HBAePGjcPnn3+O06dPY8yYMa3WNxEREbUtJpOtwNraGgCwefNmzJs3r8X2SUlJyM7OxqxZs/Dpp59q1O3evRs7duzQ6fxGRg2rFVQqVaO6qqqqZo9r7oky1tbWEAQBV69ebbK+LdjZ2QEAbty48cDOSfRIKc0Hyi8A3Z8Aejxl6GiIqANhMtkK1HdpZ2ZmapVMFhYWAgAmTJjQqC4jI6PJY4yMjJrdkkc9LXz58uVGdTk5uj8qzdvbGwcOHMC5c+fQp08fnY/Xx6lTpwDggT1RiKhDqNbiD74b5cD+BUDx8T/KZCOB5zcAlt21O09nO32iI6JHBJPJVjB06FB4e3sjLi4OEyZMwJQpUzTq6+vrkZGRAV9fXwCATCYDABw7dgxBQUFSu/T09EYjlWrdu3fHpUuXmqzz8vICAOzcuRPTp0+XRiozMzOxa9cuna9n/vz5OHDgACIjI5GYmAhbW1uN+pKSElRUVKB///469Xvq1Cl4enqiU6dOGuXr16/H8ePHMWDAAAwaNEjneInahdrWWxoiee/JltsIRoCZNfCCHOg9Arh4AvhyAfChNyDWa3eeJW24htu0c9v1TUQPBJPJVhIXFwd/f39MnToVGzZswODBg2Fubo6LFy8iMzMTZWVlqKmpAQAEBQXByckJa9asQV5eHtzc3FBQUID9+/cjODgY8fHxjfoPCAjAnj17EBoaCk9PTxgbG2P8+PFwd3fHsGHDMHz4cKSlpWH48OHw8fFBcXExkpOTERQUhISEBJ2uJTAwEMuWLcOKFSvg4uKCwMBAyGQyXLt2DefPn0dGRgZWrlypczL597//Hfn5+fD19UWvXr1w8+ZNZGZmIicnBzY2Nvj888+bnXonavfefcww5xXrgaANgOukhteukwBRBPZFaN9HW8Ye3fxSHCJqH5hMthJnZ2fk5ORg/fr1SExMxGeffQZjY2M4OjrCx8cHoaGhUlsrKyukpaVh8eLFOHr0KBQKBVxdXbFr1y7Y29s3mUxu3LgRAJCWloaEhATU19fDwcEB7u7uEAQBycnJWLhwIVJSUpCbm4tBgwYhOTkZV65c0TmZBIDly5fDx8cHmzZtQmpqKiorK2FrawtnZ2dER0dj2rRpOvf5yiuvID4+HidOnJDWY8pkMrz++utYtGgRNy4naiu9R2i+lo00TBxE1CEJoiiKhg7iYSKXyxEREYHt27drPG2GDMvPzw/p6engx5XarbaY5tZ2xPAF+R8jkwCQ91/dRiY5zU1E98Bk8i7qZFJtypQp2L17twEjenSpVKpG6yv5cSW6gzY34OyZAZQVAOPeaxiRLD4OfLUY+NNTwIta7hzBG3CI6B44zX0XDw8PREVFSa/Vm4zTg2dkZKTxsyCiu2iT5L34ORA/U3Mk8gl/IGQb0Nm2+eOIiLTEkUnSm1KphFwub7Fdt27dsGDBgjaPh4jugftMElEbYTJJelMoFPD392+xnUwmg1KpbPuAiIiI6IFjMklEREREejMydABERERE1H4xmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK98Qk41KHV1dWhvr7e0GEQERG1O3c/0rg5TCapQ/vll19QW1tr6DCIiIjanb59+2rVjskkdWi1tbUwNjaGiQk/6kRERNpSqVRat+VvWOrwTExMtB6qJyIiIt3wBhwiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0hv3mSQieoQUXbuJS1W16NnVFM62FoYOh4g6ACaTRESPgMqbKkQdVOL0L9elsiG9uiAm0AndLPirgIj0x2nuu8jlcgiCIH1NnTpVoz4rKwsBAQGwtbWFIAjw8/MzTKCPAJVKpfGzEATB0CERPXRu3q7T6mvZgSIUltfgg5efxqm3RuGDl59GYXkNog8pDX0JRNTO8c/RZkycOBEeHh5wc3OTyqqqqhAUFITa2lpMnz4dtra2cHJyeiDxCIIAX19fKBSKB3K+tlRbW4stW7bgiy++QEFBAQBAJpPB19cXH3zwgdTOyMgIUVFRABqS/OLiYoPES/QwG7X1B63bfvDy0xg/0BEAMH6gI0SImPdFDoqu3eSUNxHpjclkM4KDgxEeHq5Rdvr0aZSVlSE2NhZvvvmmYQJr5yoqKhAYGIhTp05hxIgRmDNnDgCgqKgI//nPfxolk9HR0QAAhULBZJLoPg1xttF4PdS5OwDgUlUtk0ki0huTSR1cuXIFAODg4GDgSNqvmTNn4vTp09i1axdefvlljTqVSmWgqIjar9RXB7bYRlleg5n/OYvTRRXSyCQAnCoqBwD07GraZvERUcfHNZNaEgQBYWFhAICIiAhpDd+d086lpaV444034OLiAjMzM9jZ2SEkJAR5eXmN+jty5AgiIyPRr18/WFlZwcrKCl5eXvjkk0802ikUCmmtYHp6usb6QblcDgCIjo5uFIuaeg2oui0AKJVKCIKA8PBw5OfnY/LkybCzs4MgCFAqlVK7pKQkjBo1CjY2NjA3N4ebmxvWrl2Luro6vd7DrKwsJCQk4JVXXmmUSAKAiQn/tiHSlUUn4xa/+tt3xpBeXbAsKRf7f7iC0us12P/DFbydlIehvbtwVJKI7gt/e2spKioKZ86cQVJSkrSeEoC0ZrKwsBB+fn64fPkyxowZg+DgYJSWliI+Ph6HDh1CamoqvL29pf5Wr16N8+fPY9iwYZg0aRIqKytx8OBBzJkzBwUFBVi3bp3Uf1RUFGJiYiCTyTSm3tUx6Et9fldXV4SFhaG8vBympg0jFEuWLEFsbCx69uyJkJAQWFtb4+jRo1i8eDGysrKwd+9enc/3n//8BwDwwgsv4OrVq0hOTsZvv/2GXr16YezYsbC1tb2v6yGi5sUEOiH6kBLzvsiRyob27oLo55wMFxQRdQhMJrUUHR0NuVyOpKSkJtdTzpgxAyUlJTh06BBGjx4tlS9duhReXl6YPXs2fvjhj4XyW7duhbOzs0YfKpUK48aNw8aNG/H666+jd+/ecHJyQnR0NGJiYqT/bi3Hjx/HsmXLsHz5co3yr7/+GrGxsRg7diz27dsHS0tLAIAoinjttdfw0UcfIT4+HiEhITqd79tvvwXQkMROnz4dVVVVUp2VlRX+9a9/YcqUKfd5VUTUlG4WJtgQ7MJ9Jomo1XGauxXk5OTgxIkTCAsL00gkAaBv376YPXs2cnNzNaa7704kgYZp3rlz56Kurg5Hjhxp87gdHBywdOnSRuVbtmwBAHz88cdSIgk0TPWvWrUKgiAgLi5O5/OVlpYCABYvXoyJEyeisLAQFRUV+Pe//w0jIyNMnz5dI+EmotbnbGuBPz/RlYkkEbUajky2gpMnTwIASkpKmhw5zM/Pl76rtxq6fv061q5di8TERBQWFqK6ulrjGPXNPm1p0KBB0rT2nU6ePInOnTtj27ZtTR5nYWEhXZMu6uvrAQADBw6U1nICwLRp03D9+nW8+uqr2LRpE/71r3/p3DcREREZBpPJVlBe3nBHZEpKClJSUpptp04Ya2tr4efnh+zsbHh6ekp7VpqYmECpVGLHjh24detWm8dtb2/fZHl5eTlUKhViYmKaPfbu5FcbXbt2BQA8//zzjTYgDwoKwquvvipNhRMREVH7wGSyFVhbWwMANm/ejHnz5rXYPikpCdnZ2Zg1axY+/fRTjbrdu3djx44dOp3fyKhhtUJTW+vcuS7xbs09Ucba2hqCIODq1as6xdGSfv364dtvv0W3bt0a1anLbt682arnJOpITCrOw7jqIuq69obKxsXQ4RARAWAy2SrUd2lnZmZqlUwWFhYCACZMmNCoLiMjo8ljjIyMmt2Sx8amYSPiy5cvN6rLyclpVNYSb29vHDhwAOfOnUOfPn10Pr45AQEB2LVrF3766adGdeqyB/VEISJDMrpZrlN7oaYS3Y5Fw+zX01LZLcchqHwmGqJ5txaPr7formuIRERaYzLZCoYOHQpvb2/ExcVhwoQJje5Irq+vR0ZGBnx9fQE0PDoQAI4dO4agoCCpXXp6eqORSrXu3bvj0qVLTdZ5eXkBAHbu3Inp06dLI5WZmZnYtWuXztczf/58HDhwAJGRkUhMTGy0ZU9JSQkqKirQv39/nfoNDQ3FP/7xD+zatQuvv/463N3dATRM+6sfm/jiiy/qHC9RWxFu32iTfh0+H6ljIEaAmTXwghzoPQK4eAJmXy6A/b4gQKxv8fBfI77TL9AmiJ0sW25ERI8UJpOtJC4uDv7+/pg6dSo2bNiAwYMHw9zcHBcvXkRmZibKyspQU1MDoGF9oJOTE9asWYO8vDy4ubmhoKAA+/fvR3BwMOLj4xv1HxAQgD179iA0NBSenp4wNjbG+PHj4e7ujmHDhmH48OFIS0vD8OHD4ePjg+LiYiQnJyMoKAgJCQk6XUtgYCCWLVuGFStWwMXFBYGBgZDJZLh27RrOnz+PjIwMrFy5Uudk0traGp9++ilCQ0MxbNgwhIaGwsbGBt988w1+/PFHjBs3rtGWS0SG5Lh9sKFDaCDWA0EbANdJDa9dJwGiCOyL0Orw1ryOK//3c6v1RUQdA5PJVuLs7IycnBysX78eiYmJ+Oyzz2BsbAxHR0f4+PggNDRUamtlZYW0tDQsXrwYR48ehUKhgKurK3bt2gV7e/smk8mNGzcCANLS0pCQkID6+no4ODjA3d0dgiAgOTkZCxcuREpKCnJzczFo0CAkJyfjypUrOieTALB8+XL4+Phg06ZNSE1NRWVlJWxtbeHs7Izo6GhMmzZNr/cpODgY6enpWLlyJZKTk3Hjxg306dMHq1evxsKFC2FsbKxXv0QdXu8Rmq9lOo5uEhG1EUEURdHQQTxM5HI5IiIisH37do6SPUT8/PyQnp4OXT+uZ8+ehZmZGTp16tRGkVFH1lbT3HqNFL4g/2NkEgDy/qv1yCSnuYlIV7dv35aW5bWEI5PNiIiIQEREBKZMmYLdu3cbOpxHkkqlYhJIBtVWiVPJ9OM6tbf5ZgFMU/4GQRQbRiSLj0P8ahFqHYei4tn3WzyeCSARtSUmk3fx8PCQbgYBIG0yTg+ekZGRxs+CqKPQ9e7qitEbYZO2CGZ3jETW9hyBioC1qDe3ae3wiIh0wmlu0ptSqYRcLm+xXbdu3bBgwYI2j6cpnOamjoT7TBLRg6LLNDeTSdKbQqGAv79/i+1kMhmUSmXbB9QEJpNERES645pJeiD8/Px0viGGiIiIOhYjQwdARERERO0Xk0kiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0huTSSIiIiLSG7cGog5PpVIZOgQiIqJ2RZffnUwmqUMzNTVFbW0t6urqDB0KERFRh8Qn4FCHVldXh/r6ekOHQURE1O5o+/Q4JpNEREREpDfegENEREREemMySURERER6YzJJRERERHpjMklEREREemMySURERER6YzJJRERERHrjpuVERI+Qc79dh/LaDTjZWqKPfRdDh0NEHQCTSSKiR0B5dS3mx+Xg2PmrUtkzLnbY9JInunc2NWBkRNTecdNyIqIO6kbtH8/WnSn/Fmd/u47lE90wxNkGp4sq8HZSHvo5dMG/wrw0jrM05TgDEWmPyeRd5HI5IiIipNdTpkzB7t27pddZWVn45z//ie+//x7l5eXw9fWFQqEwQKQdn0qlavQoJ35cibTn9GaKxusPXn4a4wc6Sq/3/3AF877IaXScctX4No+NiDoO/vnZjIkTJ8LDwwNubm5SWVVVFYKCglBbW4vp06fD1tYWTk5ODyQeQRDafeIaHh6OHTt23LPN8uXLsWzZMgCAkZERoqKiADQk+cXFxW0eI1FHNsTZRuP1UOfuBoqEiDoSJpPNCA4ORnh4uEbZ6dOnUVZWhtjYWLz55puGCawdCw4Objb5Xrt2Laqrq/Hcc89JZUZGRoiOjgYAKBQKJpNEOvppecO/p/Olv2PCluM4XVShMTJ5qqgcAJA8byRcelgZJEYiav+YTOrgypUrAAAHBwcDR9I+BQcHIzg4uFH5d999h5iYGLi7u2Po0KEPPjCiDkq99nFgz254xsUObyflQYSIoc7dcaqoHFFJP+LPfewwsGc3wwZKRO0a95nUkiAICAsLAwBERERAEAQIgqAx7VxaWoo33ngDLi4uMDMzg52dHUJCQpCXl9eovyNHjiAyMhL9+vWDlZUVrKys4OXlhU8++USjnUKhgCAIAID09HTpvIIgQC6XAwCio6MbxaIml8s12gKAUqmEIAgIDw9Hfn4+Jk+eDDs7OwiCAKVSKbVLSkrCqFGjYGNjA3Nzc7i5uWHt2rWoq6vT701sxr/+9S8AwMyZM1u1XyL6w6aXPDHgMWvM+yIHQ99JxbwvcjDgMWtsnOpp6NCIqJ3jyKSWoqKicObMGSQlJUnrKQFI07aFhYXw8/PD5cuXMWbMGAQHB6O0tBTx8fE4dOgQUlNT4e3tLfW3evVqnD9/HsOGDcOkSZNQWVmJgwcPYs6cOSgoKMC6deuk/qOiohATEwOZTKYx9a6OQV/q87u6uiIsLAzl5eUwNW3YImTJkiWIjY1Fz549ERISAmtraxw9ehSLFy9GVlYW9u7de1/nVrt58ybi4uJgZmaG6dOnt0qfRNRY986m+HymN/eZJKLWJ5KG7du3iwDE7du361Q3YsQI0cTERDx8+LBGeUFBgdilSxfR3d1do/zChQuN+rh9+7Y4evRo0djYWCwuLtaoAyD6+vo2GXNUVJQIQDxy5IhWMRcVFYkARADismXLGh1z+PBhEYA4duxYsbq6Wiqvr68X586dKwIQ9+3b12Qsutq5c6cIQJw6deo92/n6+or8uBIRET18OM3dCnJycnDixAmEhYVh9OjRGnV9+/bF7NmzkZubqzHd7ezs3KgfExMTzJ07F3V1dThy5Eibx+3g4IClS5c2Kt+yZQsA4OOPP4alpaVULggCVq1aBUEQEBcX1yoxbNu2DQAwa9asVumPiIiIHixOc7eCkydPAgBKSkqku4/vlJ+fL31XbzV0/fp1rF27FomJiSgsLER1dbXGMeqbfdrSoEGDpGntO508eRKdO3eWEr27WVhYSNd0P86fP4+jR4/C2dkZAQEB990fERERPXhMJltBeXnD9hopKSlISUlptp06YaytrYWfnx+ys7Ph6ekp7VlpYmICpVKJHTt24NatW20et729fZPl5eXlUKlUiImJafbYu5NffWzbtg2iKCIyMlK6yYiIiIjaFyaTrcDa2hoAsHnzZsybN6/F9klJScjOzsasWbPw6aefatTt3r27xY2972Zk1LBaQaVSNaqrqqpq9rjmEjhra2sIgoCrV682Wd8a6urqsGPHDhgbG2s8cYiIHnKl+UD5BaD7E0CPpwwdDRE9BJhMtgL1XdqZmZlaJZOFhYUAgAkTJjSqy8jIaPIYIyOjZrfksbFpeKrF5cuXG9Xl5DR+VFpLvL29ceDAAZw7dw59+vTR+XhtfPXVV/j1118xfvx4PP74421yDiJqQbUOfzDeKAf2LwCKj/9RJhsJPL8BsNTiSTqd7XSNjojaCSaTrWDo0KHw9vZGXFwcJkyYgClTpmjU19fXIyMjA76+vgAAmUwGADh27BiCgoKkdunp6Y1GKtW6d++OS5cuNVnn5eUFANi5cyemT58ujVRmZmZi165dOl/P/PnzceDAAURGRiIxMRG2trYa9SUlJaioqED//v117ltNvR6Te0sSaan2/peWNPLek9q3FYwAM2vgBTnQewRw8QTw5QLgQ29ArG/5+CVttA7ctHPb9EtEWmMy2Uri4uLg7++PqVOnYsOGDRg8eDDMzc1x8eJFZGZmoqysDDU1NQCAoKAgODk5Yc2aNcjLy4ObmxsKCgqwf/9+BAcHIz4+vlH/AQEB2LNnD0JDQ+Hp6QljY2OMHz8e7u7uGDZsGIYPH460tDQMHz4cPj4+KC4uRnJyMoKCgpCQkKDTtQQGBmLZsmVYsWIFXFxcEBgYCJlMhmvXruH8+fPIyMjAypUr9U4mf/vtN6SkpMDe3l4jmSaie3j3McOeX6wHgjYArpMaXrtOAkQR2KflMpW2ij+6+aU8RPRgMJlsJc7OzsjJycH69euRmJiIzz77DMbGxnB0dISPjw9CQ0OltlZWVkhLS8PixYtx9OhRKBQKuLq6YteuXbC3t28ymdy4cSMAIC0tDQkJCaivr4eDgwPc3d0hCAKSk5OxcOFCpKSkIDc3F4MGDUJycjKuXLmiczIJAMuXL4ePjw82bdqE1NRUVFZWwtbWFs7OzoiOjsa0adP0fq927NgBlUqFsLAwmJjwI0jUbvQeoflaNtIwcRDRQ0UQRVE0dBAPE7lcjoiICGzfvl3jaTNkWH5+fkhPTwc/rvTIaotpbl1HC1+Q/zEyCQB5/9V+ZJLT3EQdFpPJu6iTSbUpU6Zg9+7dBozo0aVSqdCpUyeNMn5ciVqRLjfg7JkBlBUA495rGJEsPg58tRj401PAi1rsQMEbcIg6LM4x3sXDwwNRUVHSa/Um4/TgGRkZafwsiKiV6ZLgvfg5ED9TcyTyCX8gZBvQ2bb544iow+PIJOlNqVRCLpe32K5bt25YsGBBm8dDRA8A95kkorswmSS9KRQK+Pv7t9hOJpNBqVS2fUBERET0wDGZJCIiIiK9GRk6ACIiIiJqv5hMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd74BBzq0Orq6lBfX2/oMIiIiNqdux9p3Bwmk9Sh/fLLL6itrTV0GERERO1O3759tWrHZJI6tNraWhgbG8PEhB91IiIibalUKq3b8jcsdXgmJiZaD9UTERGRbngDDhERERHpjckkEREREemNySQRERER6Y3JJBERERHpjckkEREREemNySQRERER6Y1bAxERPUKKrt3Epapa9OxqCmdbC0OHQ0QdAJNJIqJHQOVNFaIOKnH6l+tS2ZBeXRAT6IRuFvxVQET64zQ3EdEjYNmBIhSW1+CDl5/GqbdG4YOXn0ZheQ2iDykNHRoRtXNMJu8il8shCIL0NXXqVI36rKwsBAQEwNbWFoIgwM/PzzCBPgJUKpXGz0IQBEOHRNQuFV27ie8u/Y4VE90xfqAjenQxx/iBjlg+0Q2nLl5H0bWbhg6RiNoxzm00Y+LEifDw8ICbm5tUVlVVhaCgINTW1mL69OmwtbWFk5PTA4lHEAT4+vpCoVA8kPO1lZs3b2Lr1q3497//jaKiIoiiCJlMhhdffBHz5s1D165dpbZGRkaIiooC0JDkFxcXGypsonbtUlUtAGCIs41G+VDn7lI9108Skb6YTDYjODgY4eHhGmWnT59GWVkZYmNj8eabbxomsHbs9u3b8Pf3R1ZWFjw8PBAWFgZBEHDkyBEsXboUcXFxOHXqFCwtLQE0JJPR0dEAAIVCwWSSSE89u5oCAE4XVWD8QEep/FRRuUY9EZE+mEzq4MqVKwAABwcHA0fSPiUkJCArKwuTJ09GfHy8Rt2kSZOQmJiIffv2YcaMGQaKkKhjcra1wJBeXbAsKRciRAx17o5TReV4OykPQ3t34agkEd0XrpnUkiAICAsLAwBERERIa/junHYuLS3FG2+8ARcXF5iZmcHOzg4hISHIy8tr1N+RI0cQGRmJfv36wcrKClZWVvDy8sInn3yi0U6hUEhrBdPT0zXWD8rlcgBAdHR0o1jU1GtA1W0BQKlUQhAEhIeHIz8/H5MnT4adnR0EQYBSqZTaJSUlYdSoUbCxsYG5uTnc3Nywdu1a1NXV6fUeXrhwAQAwduzYRnXjxo0D0PAeElHriwl0goutOeZ9kYOh76Ri3hc5cLE1R/RzToYOjYjaOY5MaikqKgpnzpxBUlKStJ4SgLRmsrCwEH5+frh8+TLGjBmD4OBglJaWIj4+HocOHUJqaiq8vb2l/lavXo3z589j2LBhmDRpEiorK3Hw4EHMmTMHBQUFWLdundR/VFQUYmJiIJPJNKbe1THoS31+V1dXhIWFoby8HKamDdNdS5YsQWxsLHr27ImQkBBYW1vj6NGjWLx4MbKysrB3716dz+fq6goAOHjwIGbNmqVRd+DAAd7QRNSGulmYYEOwC/eZJKJWx2RSS9HR0ZDL5UhKSmpyPeWMGTNQUlKCQ4cOYfTo0VL50qVL4eXlhdmzZ+OHH36Qyrdu3QpnZ2eNPlQqFcaNG4eNGzfi9ddfR+/eveHk5ITo6GjExMRI/91ajh8/jmXLlmH58uUa5V9//TViY2MxduxY7Nu3T1rDKIoiXnvtNXz00UeIj49HSEiITud7/vnnERQUhPj4eAwePBi+vr4AGkZfz58/jw8//BBeXl6tc3FE1CRnWwsmkUTUqjjN3QpycnJw4sQJhIWFaSSSANC3b1/Mnj0bubm5GtPddyeSAGBiYoK5c+eirq4OR44cafO4HRwcsHTp0kblW7ZsAQB8/PHHUiIJNEz1r1q1CoIgIC4uTufzCYKAhIQELFq0CDk5OXj//ffx/vvvIycnB8HBwQgMDNT/YoiIiMggODLZCk6ePAkAKCkpaXLkMD8/X/qu3mro+vXrWLt2LRITE1FYWIjq6mqNY9Q3+7SlQYMGSdPadzp58iQ6d+6Mbdu2NXmchYWFdE26uHnzJqZOnYqsrCx88cUXGD16NARBQGpqKubPn48DBw7g5MmTePLJJ3Xum4iIiAyDyWQrKC9v2F4jJSUFKSkpzbZTJ4y1tbXw8/NDdnY2PD09pT0rTUxMoFQqsWPHDty6davN47a3t2+yvLy8HCqVCjExMc0ee3fyq43Y2FgkJycjKSkJEyZMkMpfeOEFdOnSBWPHjsXy5cuxY8cOnfsmIiIiw2Ay2Qqsra0BAJs3b8a8efNabJ+UlITs7GzMmjULn376qUbd7t27dU6mjIwaViuoVKpGdVVVVc0e19wTZaytrSEIAq5evapTHC1RJ9r+/v6N6vz9/SEIAr777rtWPSdRR2NScR7GVRdR17U3VDYuhg6HiIjJZGtQ36WdmZmpVTJZWFgIABqjc2oZGRlNHmNkZNTsljw2Ng1Ptbh8+XKjupycnBbjuZu3tzcOHDiAc+fOoU+fPjof35za2oancJSVlaFLly4adVevXoUoijAzM2u18xG1F0Y3y1tsI9RUotuxaJj9eloqu+U4BJXPREM079bi8fUW3e8nRCKiZjGZbAVDhw6Ft7c34uLiMGHCBEyZMkWjvr6+HhkZGdLdyzKZDABw7NgxBAUFSe3S09MbjVSqde/eHZcuXWqyTn0H9M6dOzF9+nRppDIzMxO7du3S+XrU6xcjIyORmJgIW1tbjfqSkhJUVFSgf//+OvU7cuRI5OXlISYmBp999hmMjY0BNLw/b7/9NoCmRy2JDE24faNN+3f4fKQWQRgBZtbAC3Kg9wjg4gmYfbkA9vuCALG+xcN/jWi7UX+xk2XLjYiow2Iy2Uri4uLg7++PqVOnYsOGDRg8eDDMzc1x8eJFZGZmoqysDDU1NQCAoKAgODk5Yc2aNcjLy4ObmxsKCgqwf/9+BAcHN3o6DAAEBARgz549CA0NhaenJ4yNjTF+/Hi4u7tj2LBhGD58ONLS0jB8+HD4+PiguLgYycnJCAoKQkJCgk7XEhgYiGXLlmHFihVwcXFBYGAgZDIZrl27hvPnzyMjIwMrV67UOZl86623kJycjJ07d+K7775DQECA9DjF3NxcODk54R//+IdOfRI9CI7bBxs6hIaEMWgD4Dqp4bXrJEAUgX0RWh3eltdw5f9+brO+iejhx2SylTg7OyMnJwfr169HYmKiNPLm6OgIHx8fhIaGSm2trKyQlpaGxYsX4+jRo1AoFHB1dcWuXbtgb2/fZDK5ceNGAEBaWhoSEhJQX18PBwcHuLu7QxAEJCcnY+HChUhJSUFubi4GDRqE5ORkXLlyRedkEgCWL18OHx8fbNq0CampqaisrIStrS2cnZ0RHR2NadOm6dxnr169kJ2djXfffRcHDhzAxx9/DEEQIJPJsHDhQixZsqTRKCgR3aH3CM3XMi1GNImI2pggiqJo6CAeJnK5HBEREdi+fXujjcnJcPz8/JCeng5dP65nz56FmZkZOnXq1EaR0aOgrae5tR41fEH+x8gkAOT9V+uRSU5zE5Eubt++LS3LawlHJpsRERGBiIgITJkyBbt37zZ0OI8klUrFJJAeCm2dLJVMP95iG5tvFsA05W8QRLFhRLL4OMSvFqHWcSgqnn2/xeOZ8BFRW2EyeRcPDw9ERUVJr9WbjNODZ2RkpPGzIOqotLnTumL0RtikLYLZHSORtT1HoCJgLerNbdoyPCKie+I0N+lNqVRCLpe32K5bt25YsGBBm8fTFE5zU0fDfSaJ6EHQZZqbySTpTaFQaLWVj0wmg1KpbPuAmsBkkoiISHdcM0kPhJ+fn843xBAREVHHYmToAIiIiIio/WIySURERER6YzJJRERERHpjMklEREREemMySURERER6YzJJRERERHrj1kDU4alUKkOHQERE1K7o8ruTySR1aKampqitrUVdXZ2hQyEiIuqQ+AQc6tDq6upQX19v6DCIiIjaHW2fHsdkkoiIiIj0xhtwiIiIiEhvTCaJiIiISG9MJomIiIhIb0wmiYiIiEhvTCaJiIiISG9MJomIiIhIb9y0nIjoEXLut+tQXrsBJ1tL9LHvYuhwiKgDYDJJRPQIKK+uxfy4HBw7f1Uqe8bFDpte8kT3zqYGjIyI2jtuWk5E1MHdqFVhpvxbnP3tOpZPdMMQZxucLqrA20l5GPCYNT6f6W3oEImoHeOaybvI5XIIgiB9TZ06VaM+KysLAQEBsLW1hSAI8PPzM0ygjwCVSqXxsxAEwdAhEbVLA94+hMwL17B8ohvGD3REjy7mGD/QETETXZFx7irO/Xbd0CESUTvGae5mTJw4ER4eHv+vvXuPi6rM/wD+OYBcBFEuJZo50KKZ4G1FUUsu+kMxQxFctfUCePllrVvmYpmrO6AVWtaqteuWW2BlUEICSQj7k4uoeNnAwjYQiMGEDOQWiynCnN8fvmZ0nEFmxhmGy+f9evHSeZ7nnOd7Dkf5cp7nPAeenp7KsqamJgQFBaG1tRXLly+Hk5MTXF1duyQeQRDg6+uLnJycLunPWBoaGvDaa68hOTkZP/74I+zt7eHr64vo6Gh4eHiotDUzM4NUKgVwK8mvrKw0RchEvcYkNweVz5PdHAEAsrprnD9JRHpjMtmB4OBghIeHq5SdO3cOtbW1iImJwaZNm0wTWA9WV1eHqVOnorS0FFOnTsX8+fPx008/ISkpCenp6cjKyoK39+3hNjMzM0RFRQEAcnJymEwS6Sl13eOY9+5JnKtowNyxQ5TlZyvqAQCuTv1NFRoR9QJMJnVQXV0NAHBxcTFxJD2TVCpFaWkpNmzYgLfeektZnp+fj+nTp2PlypUoKiqCmRlnXxAZ0thhg/CEuzP+knIBIkRMdnPE2Yp6SFO+w/QRzrwrSUT3hT+1tSQIAsLCwgAAERERyjl8dw4719TU4MUXX4S7uzusrKzg7OyM0NBQXLhwQW1/2dnZWLlyJR599FHY2dnBzs4OXl5eeP/991Xa5eTkKOcK5ubmqswfjIuLAwBERUWpxaKgmAOqaAsAMpkMgiAgPDwcxcXFCAkJgbOzMwRBgEwmU7ZLSUnBzJkz4eDgAGtra3h6emLXrl1ob2/X6xwmJyfDzMwM0dHRKuVTp05FUFAQ/vOf/yA3N1evfRPRve19egJGD7XHuk8LMfm1Y1j3aSFGD7XHniUTTB0aEfVwvDOpJalUivPnzyMlJUU5nxKAcs5keXk5/Pz8UFVVhVmzZiE4OBg1NTVISkpCRkYGjh07pjKEu3PnTpSVlWHKlClYsGABGhsbcfToUTzzzDMoKSlR3rlzdXWFVCpFdHQ0JBKJytC7IgZ9Kfr38PBAWFgY6uvrYWl5a4mQzZs3IyYmBsOGDUNoaCjs7e1x/PhxbNy4EWfOnMGhQ4d07u/nn3+Gs7Mz7Ozs1Orc3NwAAFlZWfD397+v4yIidY62lvh4lTfXmSQiwxNJRWxsrAhAjI2N1alu2rRpooWFhZiZmalSXlJSIg4YMEAcM2aMSvkPP/ygto+bN2+KAQEBorm5uVhZWalSB0D09fXVGLNUKhUBiNnZ2VrFXFFRIQIQAYhbt25V2yYzM1MEIM6ZM0dsaWlRlsvlcnHt2rUiADExMVFjLPcyZMgQ0czMTGxublarCw4OFgGIixYt0ritr6+vyMuViIio++EwtwEUFhbi1KlTCAsLQ0BAgErdyJEjsWbNGhQVFakMdyvuxN3JwsICa9euRXt7O7Kzs40et4uLC7Zs2aJW/u677wIA3nvvPfTvf3tiviAI2LFjBwRBQHx8vM79zZkzB3K5XG2Y++zZszhy5AgAoLGxUef9EhERkelwmNsATp8+DQC4cuWK8unjOxUXFyv/VCw11NzcjF27diE5ORnl5eVoaWlR2UbxsI8xjRs3TjmsfafTp0/D1tYWH3zwgcbtbGxslMeki+joaKSnp2PXrl3Iz8/HlClT8NNPPyExMRGjR4/Gt99+C3Nzc533S0RERKbDZNIA6utvLa+RlpaGtLS0DtspEsbW1lb4+fmhoKAAEyZMUK5ZaWFhAZlMhgMHDuDGjRtGj3vw4MEay+vr69HW1qZ2B/FOdye/2hg2bBjOnTsHqVSK9PR0nD17Fg8//DC2bdsGV1dXLFmyBA888IDO+yUiIiLTYTJpAPb29gCAd955B+vWreu0fUpKCgoKCrB69Wrs379fpS4hIQEHDhzQqX/FUjptbW1qdU1NTR1u19EbZezt7SEIAq5evaqx/n489NBD+Oc//6lWrrij6+XlZfA+ifq0mmKg/gfA8RHgwVGmjoaIeiEmkwageEo7Pz9fq2SyvLwcADBv3jy1ury8PI3bmJmZdbgkj4PDrbdaVFVVqdUVFhZ2Gs/dvL29kZ6ejtLSUowYMULn7XXV3t6OhIQEWFhYIDQ01Oj9EfVoLVr+knetHjiyHqg8ebtM8jjw1G6gv2Pn29s66xMdEfVBTCYNYPLkyfD29kZ8fDzmzZuHxYsXq9TL5XLk5eXB19cXACCRSAAAJ06cQFBQkLJdbm6u2p1KBUdHR1y+fFljneJu3kcffYTly5cr71Tm5+fj4MGDOh/P888/j/T0dKxcuRLJyclwcnJSqb9y5QoaGhrw2GOP6bTfmzdvoq2tDTY2NsoyuVyOyMhIlJSU4MUXX8TQoUN1jpeoW2jVfeqHXt78jXbtBDPAyh74XRwwfBpw6RTw5Xrg796AKO98+83Gn7cNS1vj90FERsdk0kDi4+Ph7++PJUuWYPfu3Zg4cSKsra1x6dIl5Ofno7a2FtevXwcABAUFwdXVFW+88QYuXLgAT09PlJSU4MiRIwgODkZSUpLa/mfMmIHPP/8cCxcuxIQJE2Bubo65c+dizJgxmDJlCqZOnYqsrCxMnToVPj4+qKysRGpqKoKCgnD48GGdjiUwMBBbt27F9u3b4e7ujsDAQEgkEtTV1aGsrAx5eXl49dVXdU4mf/75Z3h4eGDWrFlwc3NDa2srMjIyUFxcjLlz5yImJkan/RF1K693s1+ERDkQtBvwWHDrs8cCQBSBxAjttu+K44nqeBoOEfUcTCYNxM3NDYWFhXj77beRnJyMDz/8EObm5hgyZAh8fHywcOFCZVs7OztkZWVh48aNOH78OHJycuDh4YGDBw9i8ODBGpPJPXv2ALi1qPfhw4chl8vh4uKCMWPGQBAEpKamYsOGDUhLS0NRURHGjRuH1NRUVFdX65xMAsC2bdvg4+ODvXv34tixY2hsbISTkxPc3NwQFRWFpUuX6rzPgQMHYv78+Th58iSOHDmCfv36wdPTE/v378fKlSv5GkUiQxs+TfWz5HHTxEFEvZogiqJo6iC6k7i4OERERCA2NlblbTNkWn5+fsjNzQUvV+q2umqYW5c7hr+Lu31nEgAufKH9nUkOcxORlphM3kWRTCosXrwYCQkJJoyo72pra0O/fv1Uyni5Up+n7QM4n68AakuAJ9+8dUey8iTw1UbggVHAIi1WjOADOESkJQ5z32X8+PGQSqXKz4pFxqnrmZmZqXwviAjaJ3mLPgaSVqneiXzEHwj9ALB16ng7IiId8c4k6U0mkyEuLq7TdoMGDcL69euNHg8RacB1JonIyJhMkt5ycnLg7+/faTuJRAKZTGb8gIiIiKjLMZkkIiIiIr1xLRYiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0huTSSIiIiLSGxctp16tvb0dcrnc1GEQERH1OHe/ha4jTCapV/vxxx/R2tpq6jCIiIh6nJEjR2rVjskk9Wqtra0wNzeHhQUvdSIiIm21tbVp3ZY/YanXs7Cw0PpWPREREemGD+AQERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd64ziQRUR9SUfcrLje1YthAS7g52Zg6HCLqBZhMEhH1AY2/tkF6VIZzPzYryyY9PADRga4YZMMfBUSkPw5zExH1Ur/ebFd+bU2vQHn9dfzt97/F2T/PxN9+/1uU119HVIbM1GESUQ/X65PJuLg4CIKg/FqyZIlK/ZkzZzBjxgw4OTlBEAT4+fmZJlDSaNmyZSrfv7i4OFOHRNRjzNz3rfLr68v/xfb5YzB37BA8OMAac8cOwbb5njh7qRkVdb+aOlQi6sH6zNjG/PnzMX78eHh6eirLmpqaEBQUhNbWVixfvhxOTk5wdXXtkngEQYCvry9ycnK6pD9juHbtGvbt24evv/4aBQUFuHjxIkRRREVFxT3PY2lpKf785z8jOzsb//3vfzFixAj87//+L5577jmYman+fhMSEgJ3d3ecP38eKSkpRj4iot5tkpuDyufJbo4AgMtNrZw/SUR66zPJZHBwMMLDw1XKzp07h9raWsTExGDTpk2mCawHq6mpQWRkJABAIpHAwcEB9fX199zmP//5D6ZNm4Zr165h0aJFeOihh5Ceno4//vGP+Pbbb/H++++rtA8JCUFISAji4uKYTBLp6NizYwEAsvrrWPXZRZyraMDcsUOU9Wcrbv17HTbQ0iTxEVHv0OuHue+luroaAODi4mLiSHomZ2dnZGZmoq6uDjKZDJMmTep0m2effRZNTU1ITk7GJ598gp07d+Lrr7/GzJkzsX//fmRnZ3dB5ER9g00/c9j0M8djg20x6eEB2JpShCPfVqOm+TqOfFuNv6RcwOThA3hXkojuS59NJgVBQFhYGAAgIiJCOSfvzmHnmpoavPjii3B3d4eVlRWcnZ0RGhqKCxcuqO0vOzsbK1euxKOPPgo7OzvY2dnBy8tL7U5bTk4OBEEAAOTm5mqcDxgVFaUWi4JiDuidcwdlMhkEQUB4eDiKi4sREhICZ2dnCIIAmUymbJeSkoKZM2fCwcEB1tbW8PT0xK5du9De3q7XObSzs0NAQAAcHR21an/x4kUcP34c/v7+ePLJJ5Xl/fr1w2uvvQYA2L9/v16xENG9RQe6wt3JGus+LcTk145h3aeFcHeyRtRsV1OHRkQ9XJ8Z5r6bVCpVzsNTzKcEoJzrV15eDj8/P1RVVWHWrFkIDg5GTU0NkpKSkJGRgWPHjsHb21u5v507d6KsrAxTpkzBggUL0NjYiKNHj+KZZ55BSUkJ3nrrLeX+pVIpoqOjIZFIVIbeFTHoS9G/h4cHwsLCUF9fD0vLW8NXmzdvRkxMDIYNG4bQ0FDY29vj+PHj2LhxI86cOYNDhw7dV9/aUCTHs2bNUqubPHkyBg0ahNzcXKPHQdQXDbKxwO5gd64zSUQG12eTyaioKOU8PE3zKVesWIErV64gIyMDAQEByvItW7bAy8sLa9aswbfffqss37dvH9zc3FT20dbWhieffBJ79uzBCy+8gOHDh8PV1RVRUVGIjo5W/t1QTp48ia1bt2Lbtm0q5f/6178QExODOXPmIDExEf379wcAiKKI5557Dv/4xz+QlJSE0NBQg8WiSWlpKQBgxIgRanWCIMDd3R3//ve/ce3aNWWMRGRYbk42TCKJyKD67DD3vRQWFuLUqVMICwtTSSQBYOTIkVizZg2KiopUhrvvTiQBwMLCAmvXrkV7e3uXzAV0cXHBli1b1MrfffddAMB7772nkqQJgoAdO3ZAEATEx8cbPb6mpiYAwMCBAzXW29vbq7QjIiKi7q/P3pm8l9OnTwMArly5ovHOYXFxsfJPxVJDzc3N2LVrF5KTk1FeXo6WlhaVbRQP+xjTuHHjlMPadzp9+jRsbW3xwQcfaNzOxsZGeUxEREREumAyqYFieZu0tDSkpaV12E6RMLa2tsLPzw8FBQWYMGGCcs1KCwsLyGQyHDhwADdu3DB63IMHD9ZYXl9fj7a2NkRHR3e47d3JrzEo7kh2dOfxl19+AXD7DiURERF1f0wmNVAkM++88w7WrVvXafuUlBQUFBRg9erVak8jJyQk4MCBAzr1r1i4u62tTa3uXkPAiqfE72Zvbw9BEHD16lWd4jA0xVxJxdzJO4miiLKyMgwdOhS2trZdHRpRt2XRUAbzpktoHzgcbQ7upg6HiEgN50xqoHhKOz8/X6v25eXlAIB58+ap1eXl5WncxszMrMMleRwcbr2loqqqSq2usLBQq5ju5O3tjbq6Oo1JXFdSvKoyMzNTre7s2bNobGyEr69vF0dF1LXMfq3X6su84Qc4fbkCDx4KglPmH279+eUKmDf80Om2RERdiXcmNZg8eTK8vb0RHx+PefPmYfHixSr1crkceXl5ysRHIpEAAE6cOIGgoCBlu9zc3A7XTXR0dMTly5c11nl5eQEAPvroIyxfvlx5pzI/Px8HDx7U+Xief/55pKenY+XKlUhOToaTk5NK/ZUrV9DQ0IDHHntM533rYuTIkfDx8UF2dja++uor5VqTN2/eVD44tGbNGqPGQKQL4eY1g+/T5ePHtezcDLCyB34XBwyfBlw6Basv12NwYhAgyu+56U8RX99/oJ0Q+3HFBSK6hclkB+Lj4+Hv748lS5Zg9+7dmDhxIqytrXHp0iXk5+ejtrYW169fBwAEBQXB1dUVb7zxBi5cuABPT0+UlJTgyJEjCA4ORlJSktr+Z8yYgc8//xwLFy7EhAkTYG5ujrlz52LMmDGYMmUKpk6diqysLEydOhU+Pj6orKxEamoqgoKCcPjwYZ2OJTAwEFu3bsX27dvh7u6OwMBASCQS1NXVoaysDHl5eXj11Vf1SiYjIyOVw+dFRUXKMjs7OwDApk2bMGrUKGX7ffv2Ydq0aViwYAEWLVqEoUOH4ujRo/j222+xevVq+Pv76xwDkbEMiZ1ous5FORC0G/BYcOuzxwJAFIHEiE437Yq4q//3e6P3QUQ9A5PJDri5uaGwsBBvv/02kpOT8eGHH8Lc3BxDhgyBj48PFi5cqGxrZ2eHrKwsbNy4EcePH0dOTg48PDxw8OBBDB48WGMyuWfPHgBAVlYWDh8+DLlcDhcXF4wZMwaCICA1NRUbNmxAWloaioqKMG7cOKSmpqK6ulrnZBIAtm3bBh8fH+zduxfHjh1DY2MjnJyc4ObmhqioKCxdulSv85SYmIjKykqVsjuPNzw8XCWZHD16NM6ePYs///nPSE9Px3//+1+4u7tj7969+MMf/qBXDES91vBpqp8lWt7VJCLqQoIoiqKpgzCmuLg4REREIDY2Vm1hcuo59P0+Xrx4EVZWVujXr5/xgqNeyxjD3DrdNfxd3O07kwBw4Qut7kxymJuI7tfNmzeV0/g602fuTEZERCAiIgKLFy9GQkKCqcMhLS1btkyveaJEhmCMhOnK8pNatXP4v/WwTPsTBFG8dUey8iTEryLROmQyGv7nr/fclokeEXWlXp9Mjh8/HlKpVPlZscg49QwhISFwd7+9HMr9vr+cyNTkNo5atWsI2AOHrEhY3XEnsnXYNDTM2AW5tYOxwiMi0lmvH+Ym7clkMsTFxXXabtCgQVi/fr3R4zEEDnNTT8d1JonIFHQZ5mYySUo5OTlaPU0tkUggk8mMH5ABMJkkIiLSHedMkl78/PzA3y2IiIhIF3wDDhERERHpjckkEREREemNySQRERER6Y3JJBERERHpjckkEREREemNT3NTr9fW1mbqEIiIiHoUXX52MpmkXs3S0hKtra1ob283dShERES9Ehctp16tvb0dcrnc1GEQERH1ONq+8IPJJBERERHpjQ/gEBEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHe+AYcIqI+pPTnZsjqrsHVqT9GDB5g6nCIqBdgMklE1AfUt7Ti+fhCnCi7qix7wt0Ze5+eAEdbSxNGRkQ9Hd+AQ0TUByz75xl8/9Mv2DbfE5PcHHCuogF/SbmA0UPt8fEqb1OHR0Q9WK+fMxkXFwdBEJRfS5YsUak/c+YMZsyYAScnJwiCAD8/P9MEShotW7ZM5fsXFxdn6pCIepzSn5txouwqts33xNyxQ/DgAGvMHTsE0fM9kFd6FaU/N5s6RCLqwfrMMPf8+fMxfvx4eHp6KsuampoQFBSE1tZWLF++HE5OTnB1de2SeARBgK+vL3JycrqkP2O4du0a9u3bh6+//hoFBQW4ePEiRFFERUVFh+fxyy+/RGZmJgoKCnD+/Hlcu3YNUqkUUVFRGtuHhITA3d0d58+fR0pKivEOhqgXk9VdAwBMcnNQKZ/s5qis5/xJItJXn0kmg4ODER4erlJ27tw51NbWIiYmBps2bTJNYD1YTU0NIiMjAQASiQQODg6or6+/5zZvvfUWcnNzYW9vj6FDh6KsrOye7UNCQhASEoK4uDgmk0R6cnXqDwA4V9GAuWOHKMvPVtSr1BMR6aPXD3PfS3V1NQDAxcXFxJH0TM7OzsjMzERdXR1kMhkmTZrU6Tbbt2/HxYsX0djYiO3bt3dBlEQ0YvAAPOHujL+kXMCRb6tR03wdR76thjTlO0wf4cy7kkR0X/psMikIAsLCwgAAERERyjl5dw4719TU4MUXX4S7uzusrKzg7OyM0NBQXLhwQW1/2dnZWLlyJR599FHY2dnBzs4OXl5eeP/991Xa5eTkQBAEAEBubq7G+YBRUVFqsSgo5oDeOXdQJpNBEASEh4ejuLgYISEhcHZ2hiAIkMlkynYpKSmYOXMmHBwcYG1tDU9PT+zatQvt7e16nUM7OzsEBATA0dFR622mT5+OESNGKM8BEXWNvU9PwOih9lj3aSEmv3YM6z4txOih9tizZIKpQyOiHq7PDHPfTSqVKufhKeZTAlDO9SsvL4efnx+qqqowa9YsBAcHo6amBklJScjIyMCxY8fg7X37CcidO3eirKwMU6ZMwYIFC9DY2IijR4/imWeeQUlJCd566y3l/qVSKaKjoyGRSFSG3hUx6EvRv4eHB8LCwlBfXw9Ly1tLfmzevBkxMTEYNmwYQkNDYW9vj+PHj2Pjxo04c+YMDh06dF99E1H35mhriY9XeXOdSSIyPLGXi42NFQGIsbGxOtVNmzZNtLCwEDMzM1XKS0pKxAEDBohjxoxRKf/hhx/U9nHz5k0xICBANDc3FysrK1XqAIi+vr4aY5ZKpSIAMTs7W6uYKyoqRAAiAHHr1q1q22RmZooAxDlz5ogtLS3KcrlcLq5du1YEICYmJmqMRRezZ88WAYgVFRVatY+PjxcBiFKptNO29/peERERken02WHueyksLMSpU6cQFhaGgIAAlbqRI0dizZo1KCoqUhnudnNzU9uPhYUF1q5di/b2dmRnZxs9bhcXF2zZskWt/N133wUAvPfee+jf//ZEe0EQsGPHDgiCgPj4eKPHR0RERL1Pnx3mvpfTp08DAK5cuaJxyZri4mLln4qlhpqbm7Fr1y4kJyejvLwcLS0tKtsoHvYxpnHjximHte90+vRp2Nra4oMPPtC4nY2NjfKYiIiIiHTBZFIDxfI2aWlpSEtL67CdImFsbW2Fn58fCgoKMGHCBOWalRYWFpDJZDhw4ABu3Lhh9LgHDx6ssby+vh5tbW2Ijo7ucNu7k18iIiIibTCZ1MDe3h4A8M4772DdunWdtk9JSUFBQQFWr16N/fv3q9QlJCTgwIEDOvVvZnZr9kFbW5taXVNTU4fbdfSEtL29PQRBwNWrVzXWExHppKYYqP8BcHwEeHCUqaMhIhNjMqmB4int/Px8rZLJ8vJyAMC8efPU6vLy8jRuY2Zm1uGSPA4Ot95SUVVVpVZXWFjYaTx38/b2Rnp6OkpLSzFixAidtyeiXqxFh18yr9UDR9YDlSdvl0keB57aDfTXYokwW2ddoyOiHoDJpAaTJ0+Gt7c34uPjMW/ePCxevFilXi6XIy8vD76+vgBuvf0FAE6cOIGgoCBlu9zcXLU7lQqOjo64fPmyxjovLy8AwEcffYTly5cr71Tm5+fj4MGDOh/P888/j/T0dKxcuRLJyclwcnJSqb9y5QoaGhrw2GOP6bxvIjKyViNPQXnzN9q3FcwAK3vgd3HA8GnApVPAl+uBv3sDorzz7Tcbae64pa1x9ktEWmEy2YH4+Hj4+/tjyZIl2L17NyZOnAhra2tcunQJ+fn5qK2txfXr1wEAQUFBcHV1xRtvvIELFy7A09MTJSUlOHLkCIKDg5GUlKS2/xkzZuDzzz/HwoULMWHCBJibm2Pu3LkYM2YMpkyZgqlTpyIrKwtTp06Fj48PKisrkZqaiqCgIBw+fFinYwkMDMTWrVuxfft2uLu7IzAwEBKJBHV1dSgrK0NeXh5effVVvZLJyMhI5fB5UVGRsszOzg4AsGnTJowadXsYLDk5GcnJyQCAiooKZZlicfUnnngCq1ev1jkOol7r9aGmjuA2UQ4E7QY8Ftz67LEAEEUgMUK77Y11LFEdT/8hIuNjMtkBNzc3FBYW4u2330ZycjI+/PBDmJubY8iQIfDx8cHChQuVbe3s7JCVlYWNGzfi+PHjyMnJgYeHBw4ePIjBgwdrTCb37NkDAMjKysLhw4chl8vh4uKCMWPGQBAEpKamYsOGDUhLS0NRURHGjRuH1NRUVFdX65xMAsC2bdvg4+ODvXv34tixY2hsbISTkxPc3NwQFRWFpUuX6nWeEhMTUVlZqVJ25/GGh4erJJPnz59Xm0P6zTff4JtvvlF+ZjJJ1I0Nn6b6WfK4aeIgom5DEEVRNHUQxhQXF4eIiAjExsaqvG2GehZ+H6nPMvYwt653C38Xd/vOJABc+EL7O5Mc5ibqlfrMncmIiAhERERg8eLFSEhIMHU4pKVly5bpNU+UqNcwdqK0sVz7tp+vANIibw1tSx6/9SDOVxsByRPAIi1WrWDSR9Qr9fpkcvz48ZBKpcrPikXGqWcICQmBu7u78vP9vr+ciO6iyxPWiz4Gklap3ol8xB8I/QCwdep4OyLq1Xr9MDdpTyaTIS4urtN2gwYNwvr1640eDxF1U1xnkojuwGSSlHJycuDv799pO4lEonz6moiIiPo2JpNEREREpDczUwdARERERD0Xk0kiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0luvX7Sc+rb29nbI5XJTh0FERNTj9OvXT6t2TCapV/vxxx/R2tpq6jCIiIh6nJEjR2rVjskk9Wqtra0wNzeHhQUvdSIiIm21tbVp3ZY/YanXs7Cw0PpWPREREemGD+AQERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd6YTBIRERGR3phMEhEREZHemEwSERERkd64ziQRUR9SUfcrLje1YthAS7g52Zg6HCLqBZhMEhH1AY2/tkF6VIZzPzYryyY9PADRga4YZMMfBUSkP/4PQkTUS/16s135963pFahouIG//f63mOTmgHMVDdiaUoSt6RV4I+gRvfZv08/cUKESUQ/W65PJuLg4REREKD8vXrwYCQkJys9nzpzBK6+8gm+++Qb19fXw9fVFTk6OCSIlTZYtW4aDBw8qP8fGxiI8PNx0ARH1IDP3favy+W+//y3mjh0CAJg7dghEiFj3aaFaO22den7CfcdIRD1fr08mFebPn4/x48fD09NTWdbU1ISgoCC0trZi+fLlcHJygqura5fEIwhCj09cr127hn379uHrr79GQUEBLl68CFEUUVFRofE81tXVISkpCUeOHMGFCxdQVVWFAQMGYNKkSVi/fj1mz56ttk1ISAjc3d1x/vx5pKSkdMFREfVek9wcVD5PdnM0USRE1Jv0mWQyODhY7Y7WuXPnUFtbi5iYGGzatMk0gfVgNTU1iIyMBABIJBI4ODigvr6+w/aHDh3Cs88+i4ceeggzZszAQw89hMuXLyMpKQlHjx7Fm2++qdyfQkhICEJCQhAXF8dkkkhHx54dCwCQ1V/Hqs8u4lxFg/LOJACcrbj17/WDxSPh6mhtkhiJqOfrM8mkJtXV1QAAFxcXE0fSMzk7OyMzMxMTJ06Eo6MjAgMDkZGR0WH7kSNH4siRI5gzZw7MzG6vSrVlyxZ4e3tj8+bN+P3vf4+hQ4d2RfhEvZ5iTuNjg20x6eEB2JpSBBEiJrs54mxFPf6ScgGThw/AY4NtTRwpEfVkfXadSUEQEBYWBgCIiIiAIAgQBEFl2LmmpgYvvvgi3N3dYWVlBWdnZ4SGhuLChQtq+8vOzsbKlSvx6KOPws7ODnZ2dvDy8sL777+v0i4nJweCIAAAcnNzlf0KgoC4uDgAQFRUlFosCnFxcSptAUAmk0EQBISHh6O4uBghISFwdnaGIAiQyWTKdikpKZg5cyYcHBxgbW0NT09P7Nq1C+3t7Wr9aMPOzg4BAQFwdNRuqGzGjBmYO3euSiIJAI8++igWL16Mmzdv4tSpU3rFQkT3Fh3oCncna6z7tBCTXzuGdZ8Wwt3JGlGzXU0dGhH1cH32zqRUKlXOw1PMpwSgnOtXXl4OPz8/VFVVYdasWQgODkZNTQ2SkpKQkZGBY8eOwdvbW7m/nTt3oqysDFOmTMGCBQvQ2NiIo0eP4plnnkFJSQneeust5f6lUimio6MhkUhUht4VMehL0b+HhwfCwsJQX18PS0tLAMDmzZsRExODYcOGITQ0FPb29jh+/Dg2btyIM2fO4NChQ/fV9/3q168fAMDCos9ekkRGNcjGAruD3bnOJBEZXJ/9yR0VFaWch6dpPuWKFStw5coVZGRkICAgQFm+ZcsWeHl5Yc2aNfj229tPQO7btw9ubm4q+2hra8OTTz6JPXv24IUXXsDw4cPh6uqKqKgoREdHK/9uKCdPnsTWrVuxbds2lfJ//etfiImJwZw5c5CYmIj+/fsDAERRxHPPPYd//OMfSEpKQmhoqMFi0UVzczMSExNhbW2N6dOnmyQGor7CzcmGSSQRGVSfHea+l8LCQpw6dQphYWEqiSRwa97fmjVrUFRUpDLcfXciCdy6y7Z27Vq0t7cjOzvb6HG7uLhgy5YtauXvvvsuAOC9995TJpLAraH+HTt2QBAExMfHGz2+jqxduxY///wzNm/eDCcnJ5PFQURERLrrs3cm7+X06dMAgCtXrmi8c1hcXKz8U7HUUHNzM3bt2oXk5GSUl5ejpaVFZRvFwz7GNG7cOOWw9p1Onz4NW1tbfPDBBxq3s7GxUR5TV9u8eTM+/fRTBAYGYvPmzSaJgYiIiPTHZFIDxfI2aWlpSEtL67CdImFsbW2Fn58fCgoKMGHCBOWalRYWFpDJZDhw4ABu3Lhh9LgHDx6ssby+vh5tbW2Ijo7ucNu7k9+uEB0djZiYGMyYMQNffPEFzM35Ng2insSioQzmTZfQPnA42hzcTR0OEZkIk0kN7O3tAQDvvPMO1q1b12n7lJQUFBQUYPXq1di/f79KXUJCAg4cOKBT/4qnndva2tTqmpqaOtxO8ZT43ezt7SEIAq5evapTHMYUHR2NqKgo+Pn54csvv4SNDedwEZma2a8drxN7J+F6IwadiILVT+eUZTeGTELjE1EQrQd1ur3choulE/UmTCY1UDylnZ+fr1UyWV5eDgCYN2+eWl1eXp7GbczMzDpcksfB4dZbKqqqqtTqCgsLO43nbt7e3khPT0dpaSlGjBih8/aGpngAydfXF2lpaSrzOImoc8LNa0bZr8vHj2sZgBlgZQ/8Lg4YPg24dApWX67H4MQgQJR3uvlPEV/fX6BaEPvx/xWirsJkUoPJkyfD29sb8fHxmDdvHhYvXqxSL5fLkZeXB19fXwC33v4CACdOnEBQUJCyXW5urtqdSgVHR0dcvnxZY52XlxcA4KOPPsLy5cuVdyrz8/NV3lOtreeffx7p6elYuXIlkpOT1R5yuXLlChoaGvDYY4/pvG9dSaVSbNu2DdOnT2ciSaSnIbETTRuAKAeCdgMeC2599lgAiCKQGKHV5l0Rf/X/fm/0PojoFiaTHYiPj4e/vz+WLFmC3bt3Y+LEibC2tsalS5eQn5+P2tpaXL9+HQAQFBQEV1dXvPHGG7hw4QI8PT1RUlKCI0eOIDg4GElJSWr7nzFjBj7//HMsXLgQEyZMgLm5OebOnYsxY8ZgypQpmDp1KrKysjB16lT4+PigsrISqampCAoKwuHDh3U6lsDAQGzduhXbt2+Hu7s7AgMDIZFIUFdXh7KyMuTl5eHVV1/VK5mMjIxUDp8XFRUpy+zs7AAAmzZtwqhRowDcWnB927ZtsLCwwOTJk/Hmm2+q7c/Pzw9+fn46x0FEXWz4NNXPEi3vahJRr8NksgNubm4oLCzE22+/jeTkZHz44YcwNzfHkCFD4OPjg4ULFyrb2tnZISsrCxs3bsTx48eRk5MDDw8PHDx4EIMHD9aYTO7ZswcAkJWVhcOHD0Mul8PFxQVjxoyBIAhITU3Fhg0bkJaWhqKiIowbNw6pqamorq7WOZkEgG3btsHHxwd79+7FsWPH0NjYCCcnJ7i5uSEqKgpLly7V6zwlJiaisrJSpezO4w0PD1cmk4q38bS1tSkXcdeEySTRvRlrmFinO4aXTt2+MwkAlSe13rQrhrmJqOsIoiiKpg7CmOLi4hAREYHY2Fi1hcmp59D3+3jx4kVYWVkp37BDRB3T9gEch/9bD8umHyA8uevWHcnKkxC/ikTrwN+g4X/+2un2fACHqPu7efOmchpfZ/rMncmIiAhERERg8eLFSEhIMHU4pKVly5bpNU+UiHSnbZLXELAHDlmRsLpjjmTrsGlomLELcmsHY4VHRN1Ur08mx48fD6lUqvysWGSceoaQkBC4u99ev+5+319ORPdPbu2Auic/4DqTRASgDwxzk/ZkMhni4uI6bTdo0CCsX7/e6PEYAoe5iYiIdKfLMDeTSVLKycmBv79/p+0kEonyYZrujskkERGR7jhnkvTi5+cH/m5BREREujAzdQBERERE1HMxmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivfFpbur12traTB0CERFRj6LLz04mk9SrWVpaorW1Fe3t7aYOhYiIqFfiouXUq7W3t0Mul5s6DCIioh5H2xd+MJkkIiIiIr3xARwiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0huTSSIiIiLSG5NJIiIiItIbk0kiIiIi0hvfgENE1IeU/twMWd01uDr1x4jBA0wdDhH1AkwmiYj6gPqWVjwfX4gTZVeVZU+4O2Pv0xPgaGtpwsiIqKfjG3CIiPqAp98/jYs/N2PbfE9McnPAuYoG/CXlAkYPtcfHq7xNHR4R9WC9fs5kXFwcBEFQfi1ZskSl/syZM5gxYwacnJwgCAL8/PxMEyhptGzZMpXvX1xcnKlDIupxSn9uRv4Pddg23xNzxw7BgwOsMXfsEETP90Be6VWU/txs6hCJqAfrM8Pc8+fPx/jx4+Hp6aksa2pqQlBQEFpbW7F8+XI4OTnB1dW1S+IRBAG+vr7Iycnpkv6M4dq1a9i3bx++/vprFBQU4OLFixBFERUVFRrPoyiKeOmll3Du3DlcvHgR9fX1GDhwIH7zm99g1apVWLFihdpL5UNCQuDu7o7z588jJSWli46MqHeR1V0DAExyc1Apn+zmqKzn/Eki0lefSSaDg4MRHh6uUnbu3DnU1tYiJiYGmzZtMk1gPVhNTQ0iIyMBABKJBA4ODqivr++wfXt7O9555x14eXlh7ty5eOCBB9DQ0ICjR49i9erVOHToEL766iuYmd2+YR4SEoKQkBDExcUxmSTSk6tTfwDAuYoGzB07RFl+tqJepZ6ISB99JpnUpLq6GgDg4uJi4kh6JmdnZ2RmZmLixIlwdHREYGAgMjIyOmxvYWGBxsZGWFtbq5S3tbVh1qxZyMjIQHp6OubOnWvs0In6lBGDB+AJd2f8JeUCRIiY7OaIsxX1kKZ8h+kjnHlXkojuS6+fM9kRQRAQFhYGAIiIiFDOybtz2LmmpgYvvvgi3N3dYWVlBWdnZ4SGhuLChQtq+8vOzsbKlSvx6KOPws7ODnZ2dvDy8sL777+v0i4nJweCIAAAcnNzNc4HjIqKUotFQTEH9M65gzKZDIIgIDw8HMXFxQgJCYGzszMEQYBMJlO2S0lJwcyZM+Hg4ABra2t4enpi165daG9v1+sc2tnZISAgAI6Ojlpvc3ciCdxKMoODgwEAZWVlesVCRPe29+kJGD3UHus+LcTk145h3aeFGD3UHnuWTDB1aETUw/XZO5NSqVQ5D08xnxKAcq5feXk5/Pz8UFVVhVmzZiE4OBg1NTVISkpCRkYGjh07Bm/v209A7ty5E2VlZZgyZQoWLFiAxsZGHD16FM888wxKSkrw1ltvKfcvlUoRHR0NiUSiMvSuiEFfiv49PDwQFhaG+vp6WFreWvJj8+bNiImJwbBhwxAaGgp7e3scP34cGzduxJkzZ3Do0KH76vt+yOVyHD16FABU5rQSkeE42lri41XeXGeSiAxP7OViY2NFAGJsbKxOddOmTRMtLCzEzMxMlfKSkhJxwIAB4pgxY1TKf/jhB7V93Lx5UwwICBDNzc3FyspKlToAoq+vr8aYpVKpCEDMzs7WKuaKigoRgAhA3Lp1q9o2mZmZIgBxzpw5YktLi7JcLpeLa9euFQGIiYmJGmPRxezZs0UAYkVFRadtpVKpKJVKxT/84Q/iqFGjRABieHh4h+3v9b0iIiIi0+mzw9z3UlhYiFOnTiEsLAwBAQEqdSNHjsSaNWtQVFSkMtzt5uamth8LCwusXbsW7e3tyM7ONnrcLi4u2LJli1r5u+++CwB477330L//7Yn2giBgx44dEAQB8fHxRo/vTtHR0YiOjsbf/vY3lJSUIDIyEvv37+/SGIiIiOj+9dlh7ns5ffo0AODKlSuIiopSqy8uLlb+qRiWbW5uxq5du5CcnIzy8nK0tLSobKN42MeYxo0bpxzWvtPp06dha2uLDz74QON2NjY2ymPqKqIoQi6Xo7q6GkeOHMErr7yC/Px8fPXVV7C3t+/SWIiIiEh/TCY1UCxvk5aWhrS0tA7bKRLG1tZW+Pn5oaCgABMmTFCuWWlhYQGZTIYDBw7gxo0bRo978ODBGsvr6+vR1taG6OjoDre9O/ntCmZmZhg2bBjWrl0LJycnLFq0CK+99hp27tzZ5bEQ9Qo1xUD9D4DjI8CDo0wdDRH1EUwmNVDcGXvnnXewbt26TtunpKSgoKAAq1evVhuqTUhIwIEDB3TqX7HOYltbm1pdU1NTh9spnhK/m729PQRBwNWrVzXWdwezZs0CgB69iDuRwbVo+W/2Wj1wZD1QefJ2meRx4KndQH8tVluwddYnOiIiAEwmNVI8pZ2fn69VMlleXg4AmDdvnlpdXl6exm3MzMw6XJLHweHWWyqqqqrU6goLCzuN527e3t5IT09HaWkpRowYofP2XUExDcDCgpck9UCtRrqz/+ZvtGsnmAFW9sDv4oDh04BLp4Av1wN/9wZEeefbbzbiNBxLW+Ptm4i6Bf7k1mDy5Mnw9vZGfHw85s2bh8WLF6vUy+Vy5OXlwdfXF8Ctt78AwIkTJxAUFKRsl5ub2+FDJY6Ojrh8+bLGOi8vLwDARx99hOXLlyvvVObn5+PgwYM6H8/zzz+P9PR0rFy5EsnJyXByclKpv3LlChoaGvDYY4/pvG9dFBcXw9HREQ8++KBK+bVr17BhwwYAwJw5c4waA5FRvD7UtP2LciBoN+Cx4NZnjwWAKAKJEdptb8z4ozoeTSGi3oHJZAfi4+Ph7++PJUuWYPfu3Zg4cSKsra1x6dIl5Ofno7a2FtevXwcABAUFwdXVFW+88QYuXLgAT09PlJSU4MiRIwgODkZSUpLa/mfMmIHPP/8cCxcuxIQJE2Bubo65c+dizJgxmDJlCqZOnYqsrCxMnToVPj4+qKysRGpqKoKCgnD48GGdjiUwMBBbt27F9u3b4e7ujsDAQEgkEtTV1aGsrAx5eXl49dVX9UomIyMjlcPnRUVFyjI7OzsAwKZNmzBq1K25W0ePHsXLL78MPz8/PPLIIxg4cCCqqqqQnp6Ouro6PP7448qkkoh0NHya6mfJ46aJg4j6HCaTHXBzc0NhYSHefvttJCcn48MPP4S5uTmGDBkCHx8fLFy4UNnWzs4OWVlZ2LhxI44fP46cnBx4eHjg4MGDGDx4sMZkcs+ePQCArKwsHD58GHK5HC4uLhgzZgwEQUBqaio2bNiAtLQ0FBUVYdy4cUhNTUV1dbXOySQAbNu2DT4+Pti7dy+OHTuGxsZGODk5wc3NDVFRUVi6dKle5ykxMRGVlZUqZXceb3h4uDKZ/J//+R+sWrUKJ06cwLlz59Dc3IyBAwfC09MTS5YswerVqznMTT2TsYaJdbljeOnU7TuTgOr8yc4Yc5ibiHo9QRRF0dRBGFNcXBwiIiIQGxur8rYZ6ln4faQ+SdsHcD5fAdSWAE++eeuOZOVJ4KuNwAOjgEVaPADIB3CI6D70mdtAERERiIiIwOLFi5GQkGDqcEhLy5Yt02ueKFGvoG2St+hjIGmV6hzJR/yB0A8AW6eOtyMiMoBen0yOHz8eUqlU+Znvfu5ZQkJC4O7urvx8v+8vJ+qVbJ2AFclcZ5KITKLXD3OT9mQyGeLi4jptN2jQIKxfv97o8RAREVH3x2SSlHJycuDv799pO4lEAplMZvyAiIiIqNtjMklEREREejMzdQBERERE1HMxmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK99fo34FDfJYoimpubTR0GERFRjzVgwAAIgnDPNkwmqddqbm7GwIEDTR0GERFRj9XU1AR7e/t7tuGi5dRrdYc7k7/88gsefvhh/Pjjj53+Y6R747k0DJ5Hw+B5NAyeR8Mw5nnknUnq0wRB6Db/Odnb23ebWHo6nkvD4Hk0DJ5Hw+B5NAxTnUc+gENEREREemMySURERER6YzJJZERWVlaQSqWwsrIydSg9Hs+lYfA8GgbPo2HwPBqGqc8jH8AhIiIiIr3xziQRERER6Y3JJBERERHpjckkEREREemNySQRERER6Y3JJJEerly5gtWrV2PIkCGwtrbGyJEjsW3bNrS2tuq0H0EQOvzasWOHUfvuDgxxLKWlpXj99dfh4+ODoUOHwtLSEg8//DBWrFiB4uJijduEh4d3eN5HjRplqMMzqHPnzuHJJ5+Eg4MDbG1tMXnyZHz66ac67UMul+Pdd9/F2LFjYWNjgwceeACLFi1CaWmpUfvtTu73eE6cOIE//elPmDhxIpycnGBtbY1Ro0bh5ZdfRmNjo8ZtXF1dO7ze1q5da6Aj61r3ex5zcnLu+f/f6dOnjdJvd3S/x+Tn53fPcykIAj7++GOVbQx9TfINOEQ6unLlCry9vfHjjz8iODgYI0eOxIkTJyCVSpGfn4+0tDSYmWn/e5pEIkF4eLha+RNPPGH0vk3JUMeydetWfPbZZ/D09MT8+fNhb2+PoqIifPzxx0hMTERGRgamT5+ucdsXXngBgwYNUilzdnY2xOEZVE5ODmbPng1LS0ssWbIEAwcOxBdffIGlS5dCJpNh8+bNWu1n7dq12L9/P0aPHo0//vGP+Pnnn/HZZ58hMzMTp06dwujRo43Sb3dhiONZuHAhrl69iieeeAIrVqyAIAjIycnBG2+8gaSkJJw6dQoPPvig2nYDBw7E+vXr1cq9vLwMcWhdypDXha+vL/z8/NTKhw0bZtR+uwtDHFN4eLjGc3jz5k3ExMTAzMwMM2fOVKs36DUpEpFOVqxYIQIQ//73vyvL5HK5GBYWJgIQP/zwQ633BUD09fU1Sd+mZqhjiY2NFc+fP69WHh8fLwIQR48erVan6KOiokLv+LvKzZs3xd/85jeilZWVWFBQoCz/5ZdfRA8PD9HCwkK8ePFip/vJysoSAYjTp08Xr1+/riz/v//7P1EQBNHHx8co/XYXhjqeHTt2iNXV1SplcrlcfPbZZ0UA4nPPPae2jUQiESUSyX0fQ3dgqPOYnZ0tAhClUmmX9tudGPuYEhMTRQBiUFCQWp2hr0kmk0Q6+OWXX0QrKyvxkUceEeVyuUpddXW1aGZmJk6dOlXr/emSTBq6b1PqqmMZOXKkCECsra1VKe9JyWRGRoYIQIyIiFCrS0hIEAGIr7zySqf7efrpp0UAYm5urlpdYGCgCEAsKSkxeL/dhbGPp7q6WgQgenh4qNX1pmTSUOdR12Syt12Pomj8Y1L8u05OTlarM/Q1yWFuIh3k5+fjxo0bCAgIgCAIKnVDhgzBmDFjcObMGVy/fh3W1tZa7bOxsRH//Oc/UVNTgwceeAB+fn4YMWJEl/RtKl11LP369QMAWFho/q8uLS0Nzc3NsLKywtixY+Hn5wdzc3O9+zOGnJwcAMCsWbPU6hRlubm5Wu3H1tYWjz/+uFrd7NmzcfToUeTm5mLkyJEG7be7MPbxdHat3bhxAwcOHEBVVRUcHBwwbdo0jBs3Tu/+TMXQ57G0tBR79+7FtWvXIJFIEBAQoHGqSW+7HgHjHtPly5eRmZkJFxcXzJ07V2MbQ16TTCaJdKB4UEFTsqco/+abb/DDDz+ozT/ryDfffIM1a9YoPwuCgKVLl+K9995D//79jdq3qXTFsZw9exbfffcdJk2apDYvUmHdunUqn0eOHIn4+Hj89re/1atPY7jXuXJwcICzs/M9H6ABgJaWFvz000/w9PTUmCwr9n3nfgzRb3di7OP58MMPAWhODIBbc4TvnhsdGBiIjz/+uFvO0+2Ioc/jp59+qvKwiY2NDaKjo7Fx40aj9tsdGPOYYmNjIZfLER4e3uEvOIa8JnvGTH2ibqKpqQnArYnLmtjb26u060xkZCTOnDmD+vp6NDQ0ICsrC97e3vjkk0+watUqo/ZtSsY+lqamJoSFhcHMzAxvvPGGWr2vry+SkpLw448/4tdff8X333+P9evXo7y8HLNmzUJ1dbVe/RqDNueqs/Okz/k2RL/diTGP5/z584iOjsaDDz6Il156Sa1+5cqVyMnJQW1tLX755RecPn0ac+bMwdGjRzFv3jyIPeitxoY6jw888ADefPNNfP/992hpaUFVVRU++eQTODo64qWXXsJ7771nlH67E2MdkyiKiI2NBQC1nyMKhr4mmUxSn+Ts7NzpUgp3fimGIwztzTffxOTJk+Hg4IBBgwbB398fx44dg7u7OxISEvDdd98ZpV9D6S7n8U7Xr19HSEgIiouLsX37do1POUZERCAkJATDhg1TLu3y17/+FS+//DLq6urw17/+1ehxUu9QUVGBp556Cu3t7UhISNB4R+cvf/kLfH194ezsjAEDBsDb2xtHjhzBE088gfz8fHz11VcmiNy0PDw8EBkZiVGjRqF///4YOnQoli5diqNHj8LS0hJSqRRyudzUYfZIWVlZqKiogK+vL9zd3TW2MfQ1yWFu6pOefvppNDc3a93excUFwO3fIDv6bfGXX35RaaeP/v374+mnn8b27dtx8uRJeHh4dFnfuupu5/HGjRtYsGABsrKy8Morr+i8VMiqVavw+uuv4+TJkzptZ0zanKvOzpM+59sQ/XYnxjieyspK+Pv7o7a2FklJSfD399d6WzMzM0RERODEiRM4efJkh/PauhtjXxeenp7w9vZGXl4eysrKlHN4e9v1CBjvmP75z38CAFavXq3TdvdzTTKZpD7pnXfe0Ws7TXPL7lRaWgozMzM88sgjescG3F7r8Nq1a13ety6603m8fv06goODkZGRgZdeegmvv/66znFpOu+mdue5mjhxokpdQ0MDrl69imnTpt1zH7a2thgyZAgqKirQ3t6uNm9S09wtQ/TbnRj6eGQyGfz9/VFdXY1Dhw7hqaee0jmm7ni9daYrrovO/v/rDdcjYJxjamhowOHDhzFo0CCEhobqHJO+1ySHuYl0MGXKFFhZWeFf//qX2pySn376CUVFRfD29r7vp6nPnDkD4NZbCrq6765g6GO5M5GMjIzEzp079YpL03k3NV9fXwBAZmamWp2iTNGms/20tLRovOuakZGhth9D9dtdGPJ4ZDIZ/Pz8UFVVhc8++wzz58/XK6bueL11xtjXRVtbGwoKCiAIAoYPH95l/ZqCMY7pk08+wY0bN7B06VLY2NjoHJPe16TBFhki6iN0XWy7paVF/P7778XKykqV8oKCArGlpUVt/59//rkoCILo7OwsNjc331ff3ZmhzuOvv/4qzpo1SwQgbtiwodN+f/rpJ7GsrEyt/PLly+KoUaNEAGJCQoKeR2V4N2/eFB955BHRyspKLCwsVJbfubDxnetD1tbWit9//73a2pp3Llp+48YNZfm9Fi3Xpd/uzlDnsaKiQpRIJKKFhYWYlJTUab/fffed2NDQoFael5cnWltbi1ZWVmrXdHdmqPN46tQptTVmb968Ka5fv14EIAYGBt5Xvz2Boc7lncaNGycCUFkE/W7GuCaZTBLpqLq6Wnz44YdFQRDEkJAQcdOmTeLjjz8uAhBnz54ttre3q7RXLM579+LkYWFh4sCBA8WQkBBx/fr14gsvvCBOnz5dBCBaW1uLaWlp9913d2bI8whAdHFxEaVSqcavOxcnz87OFgVBEKdPny6uWbNGfPnll8XFixeLtra2IgAxLCxM7YecqWVlZYn9+vUT7ezsxDVr1oh/+tOfRDc3NxGA+Oqrr6q0lUqlHS4GvXr1auVbgTZu3CiuWLFCtLKyEgcOHCh+991399VvT2CI8yiRSEQA4pQpUzq83u7ej42NjfjUU0+J69atE//0pz+Js2fPFgVBEM3NzcX9+/cb+agNz1Dn0dXVVfz9738vbty4UVyzZo346KOPigDE4cOHizKZ7L767SkM9W9bFEXx3//+twhA/O1vf3vPPo1xTTKZJNJDdXW1uHLlSnHw4MGipaWl6O7uLkZHR6u8pk6hoyToiy++EOfPny+6urqK/fv3Fy0tLUU3Nzdx1apV4vfff2+Qvrs7Q5xHX19fEcA9v7Kzs5XtL126JK5evVocO3as6ODgIFpYWIhOTk5iQEBAt7ojebczZ86IgYGB4sCBA0UbGxvRy8tL/OSTT9Ta3esHTnt7u7h3717Rw8NDtLKyEp2cnMSFCxfe846Otv32FPd7Hju71u4e8MvJyREXLVokuru7iwMGDBD79esnDhs2TFyyZIl45swZYx6qUd3vedyxY4fo5+cnDh06VLS0tBT79+8vjh07Vvzzn/8s1tfX33e/PYkh/m2Loqh8peedoz2aGOOaFESxBy1wRURERETdCh/AISIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK9MZkkIiIiIr0xmSQiIiIivTGZJCIiIiK9/T9eyXgymtklgQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_t\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "CausalPy",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From db0522e5dc4ce5f5fe2f703bd1036e8ee54ecb37 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 21:04:09 +0000
Subject: [PATCH 03/24] trying to fix doctests

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 38 ++++++++++-----------------
 1 file changed, 14 insertions(+), 24 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index c4b04622..d13ad8f8 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -41,14 +41,14 @@ def _relaxed_bernoulli_transform(
     Parameters
     ----------
     p : float or PyMC variable
-        Probability parameter
+        Probability parameter.
     temperature : float, default=0.1
-        Temperature parameter (lower = more binary)
+        Temperature parameter (lower = more binary).
 
     Returns
     -------
     function
-        Transform function that takes uniform random variable
+        Transform function that takes uniform random variable.
     """
 
     def transform(u):
@@ -84,6 +84,8 @@ class SpikeAndSlabPrior:
 
     Example
     -------
+    >>> import pymc as pm
+    >>> from causalpy.variable_selection_priors import SpikeAndSlabPrior
     >>> spike_slab = SpikeAndSlabPrior(dims="features")
     >>> with pm.Model():
     ...     beta = spike_slab.create_variable("beta")
@@ -161,6 +163,8 @@ class HorseshoePrior:
 
     Example
     -------
+    >>> import pymc as pm
+    >>> from causalpy.variable_selection_priors import HorseshoePrior
     >>> horseshoe = HorseshoePrior(dims="features")
     >>> with pm.Model():
     ...     beta = horseshoe.create_variable("beta")
@@ -261,18 +265,11 @@ class VariableSelectionPrior:
     -------
     >>> import pymc as pm
     >>> from variable_selection_priors import VariableSelectionPrior
-    >>>
     >>> # Create spike-and-slab prior
     >>> vs_prior = VariableSelectionPrior("spike_and_slab")
-    >>>
     >>> with pm.Model() as model:
     ...     # Create coefficients with variable selection
-    ...     beta = vs_prior.create_prior(
-    ...         name="beta",
-    ...         n_params=5,
-    ...         dims="features",
-    ...         X=X_train,  # For computing tau0 in horseshoe
-    ...     )
+    ...     beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
     """
 
     def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None):
@@ -375,11 +372,13 @@ def create_prior(
 
         Example
         -------
-        >>> vs_prior = VariableSelectionPrior("horseshoe")
+        >>> import pymc as pm
+        >>> import pandas as pd
+        >>> from variable_selection_priors import VariableSelectionPrior
+        >>> X_train = pd.DataFrame{'x': [1, 2, 3, 4]}
+        >>> vs_prior = VariableSelectionPrior("spike_and_slab")
         >>> with pm.Model() as model:
-        ...     beta = vs_prior.create_prior(
-        ...         "beta", n_params=10, dims="features", X=X_train
-        ...     )
+        ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")
         """
         # Merge instance and call-specific hyperparameters
         default_hp = self._get_default_hyperparams(n_params, X)
@@ -450,11 +449,6 @@ def get_inclusion_probabilities(
         ValueError
             If prior_type is not 'spike_and_slab' or gamma variables not found
 
-        Example
-        -------
-        >>> result = vs_prior.get_inclusion_probabilities(idata, "beta")
-        >>> print(f"Selected features: {result['selected']}")
-        >>> print(f"Inclusion probs: {result['probabilities']}")
         """
         if self.prior_type != "spike_and_slab":
             raise ValueError(
@@ -512,10 +506,6 @@ def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]
         ValueError
             If prior_type is not 'horseshoe' or required variables not found
 
-        Example
-        -------
-        >>> result = vs_prior.get_shrinkage_factors(idata, "beta")
-        >>> print(f"Shrinkage factors: {result['shrinkage_factors']}")
         """
         if self.prior_type != "horseshoe":
             raise ValueError("Shrinkage factors only available for 'horseshoe' priors")

From 1670af48836fd379a627847ea633124c8696ce0d Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 21:11:15 +0000
Subject: [PATCH 04/24] adding fix

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index d13ad8f8..f5e6e5b8 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -375,7 +375,6 @@ def create_prior(
         >>> import pymc as pm
         >>> import pandas as pd
         >>> from variable_selection_priors import VariableSelectionPrior
-        >>> X_train = pd.DataFrame{'x': [1, 2, 3, 4]}
         >>> vs_prior = VariableSelectionPrior("spike_and_slab")
         >>> with pm.Model() as model:
         ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")
@@ -568,14 +567,6 @@ def create_variable_selection_prior(
     PyMC variable
         The coefficient vector with specified prior
 
-    Example
-    -------
-    >>> with pm.Model() as model:
-    ...     X = pm.Data("X", X_train)
-    ...     beta = create_variable_selection_prior(
-    ...         "spike_and_slab", "beta", n_params=X_train.shape[1], dims="features"
-    ...     )
-    ...     mu = pm.math.dot(X, beta)
     """
     vs_prior = VariableSelectionPrior(prior_type, hyperparams)
     return vs_prior.create_prior(name, n_params, dims, X)

From 83061e42840d53a89b9bea537d5c87039c171ce3 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 21:18:47 +0000
Subject: [PATCH 05/24] another fix

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index f5e6e5b8..4f8a39a3 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -87,7 +87,8 @@ class SpikeAndSlabPrior:
     >>> import pymc as pm
     >>> from causalpy.variable_selection_priors import SpikeAndSlabPrior
     >>> spike_slab = SpikeAndSlabPrior(dims="features")
-    >>> with pm.Model():
+    >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+    >>> with pm.Model(coords=coords) as model:
     ...     beta = spike_slab.create_variable("beta")
     """
 
@@ -166,7 +167,8 @@ class HorseshoePrior:
     >>> import pymc as pm
     >>> from causalpy.variable_selection_priors import HorseshoePrior
     >>> horseshoe = HorseshoePrior(dims="features")
-    >>> with pm.Model():
+    >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+    >>> with pm.Model(coords=coords) as model:
     ...     beta = horseshoe.create_variable("beta")
     """
 
@@ -264,10 +266,11 @@ class VariableSelectionPrior:
     Example
     -------
     >>> import pymc as pm
-    >>> from variable_selection_priors import VariableSelectionPrior
+    >>> from causalpy.variable_selection_priors import VariableSelectionPrior
     >>> # Create spike-and-slab prior
     >>> vs_prior = VariableSelectionPrior("spike_and_slab")
-    >>> with pm.Model() as model:
+    >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+    >>> with pm.Model(coords=coords) as model:
     ...     # Create coefficients with variable selection
     ...     beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
     """
@@ -374,7 +377,7 @@ def create_prior(
         -------
         >>> import pymc as pm
         >>> import pandas as pd
-        >>> from variable_selection_priors import VariableSelectionPrior
+        >>> from causalpy.variable_selection_priors import VariableSelectionPrior
         >>> vs_prior = VariableSelectionPrior("spike_and_slab")
         >>> with pm.Model() as model:
         ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")

From 05dc20d5df29490ff9f30843cd845caad8d71bf2 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 22:07:47 +0000
Subject: [PATCH 06/24] update fix

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index 4f8a39a3..fdac7ff4 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -379,7 +379,8 @@ def create_prior(
         >>> import pandas as pd
         >>> from causalpy.variable_selection_priors import VariableSelectionPrior
         >>> vs_prior = VariableSelectionPrior("spike_and_slab")
-        >>> with pm.Model() as model:
+        >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+        >>> with pm.Model(coords=coords) as model:
         ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")
         """
         # Merge instance and call-specific hyperparameters

From 73e6a8dc958659a90e496690dc81bd1567b1cbcd Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 22:41:10 +0000
Subject: [PATCH 07/24] adding tests

Signed-off-by: Nathaniel 
---
 causalpy/experiments/instrumental_variable.py |   4 +-
 .../tests/test_variable_selection_priors.py   | 114 ++++++++++++++++++
 docs/source/_static/interrogate_badge.svg     |   6 +-
 3 files changed, 119 insertions(+), 5 deletions(-)
 create mode 100644 causalpy/tests/test_variable_selection_priors.py

diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py
index 67852123..5b6b4128 100644
--- a/causalpy/experiments/instrumental_variable.py
+++ b/causalpy/experiments/instrumental_variable.py
@@ -51,10 +51,10 @@ class InstrumentalVariable(BaseExperiment):
         If priors are not specified we will substitute MLE estimates for
         the beta coefficients. Example: ``priors = {"mus": [0, 0],
         "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}``.
-    :param vs_prior_type : str or None, default=None
+    vs_prior_type : str or None, default=None
         Type of variable selection prior: 'spike_and_slab', 'horseshoe', or None.
         If None, uses standard normal priors.
-    :param vs_hyperparams : dict, optional
+    vs_hyperparams : dict, optional
         Hyperparameters for variable selection priors. Only used if vs_prior_type
         is not None.
 
diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py
new file mode 100644
index 00000000..09de5885
--- /dev/null
+++ b/causalpy/tests/test_variable_selection_priors.py
@@ -0,0 +1,114 @@
+#   Copyright 2022 - 2025 The PyMC Labs Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+import numpy as np
+import pymc as pm
+import pytest
+
+from causalpy.variable_selection_priors import (
+    HorseshoePrior,
+    SpikeAndSlabPrior,
+    VariableSelectionPrior,
+    create_variable_selection_prior,
+)
+
+
+@pytest.fixture
+def sample_data():
+    """Generate sample design matrix for testing."""
+    rng = np.random.default_rng(42)
+    n_obs = 100
+    n_features = 5
+    X = rng.normal(size=(n_obs, n_features))
+    return X
+
+
+@pytest.fixture
+def coords():
+    """Generate sample coordinates for PyMC models."""
+    return {"features": [f"x_{i}" for i in range(5)]}
+
+
+def test_create_variable_in_model_context(coords):
+    """Test that create_variable works in PyMC model context."""
+    prior = SpikeAndSlabPrior(dims="features")
+
+    with pm.Model(coords=coords) as model:
+        beta = prior.create_variable("beta")
+
+        # Check that beta was created
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+        # Check that intermediate variables were created
+        assert "pi_beta" in model.named_vars
+        assert "beta_raw" in model.named_vars
+        assert "gamma_beta" in model.named_vars
+
+
+def test_create_variable_in_model_context_horseshoe(coords):
+    """Test that create_variable works in PyMC model context."""
+    prior = HorseshoePrior(dims="features")
+
+    with pm.Model(coords=coords) as model:
+        beta = prior.create_variable("beta")
+
+        # Check that beta was created
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+        # Check that intermediate variables were created
+        assert "tau_beta" in model.named_vars
+        assert "lambda_beta" in model.named_vars
+        assert "c2_beta" in model.named_vars
+        assert "lambda_tilde_beta" in model.named_vars
+        assert "beta_raw" in model.named_vars
+
+
+def test_create_prior_spike_and_slab(coords):
+    """Test create_prior for spike-and-slab."""
+    vs_prior = VariableSelectionPrior("spike_and_slab")
+
+    with pm.Model(coords=coords) as model:
+        beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
+
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+
+def test_create_prior_horseshoe(coords, sample_data):
+    """Test create_prior for horseshoe."""
+    vs_prior = VariableSelectionPrior("horseshoe")
+
+    with pm.Model(coords=coords) as model:
+        beta = vs_prior.create_prior(
+            name="beta", n_params=5, dims="features", X=sample_data
+        )
+
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+
+def test_convenience_function_with_custom_hyperparams(coords):
+    """Test convenience function with custom hyperparameters."""
+    with pm.Model(coords=coords) as model:
+        _ = create_variable_selection_prior(
+            prior_type="spike_and_slab",
+            name="beta",
+            n_params=5,
+            dims="features",
+            hyperparams={"slab_sigma": 5},
+        )
+
+        assert "beta" in model.named_vars
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index d2d886ad..a625f245 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,5 +1,5 @@
 
-    interrogate: 95.4%
+    interrogate: 95.2%
     
         
             
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        95.4%
-        95.4%
+        95.2%
+        95.2%
     
     
         

From 8d6251fdc092c3ad70ebf869c40926b2ede69ad0 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 09:21:02 +0000
Subject: [PATCH 08/24] update adding more tests

Signed-off-by: Nathaniel 
---
 causalpy/pymc_models.py                       |   16 +-
 .../tests/test_integration_pymc_examples.py   |   39 +
 causalpy/variable_selection_priors.py         |   56 +-
 docs/source/_static/interrogate_badge.svg     |    8 +-
 docs/source/notebooks/iv_vs_priors.ipynb      | 1467 ++++++++---------
 5 files changed, 798 insertions(+), 788 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index d0c04bfd..7d30dd5a 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -684,9 +684,9 @@ def build_model(  # type: ignore
             Dictionary of priors for the mus and sigmas of both
             regressions. Example: ``priors = {"mus": [0, 0],
             "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}``.
-        :param vs_prior_type: An optional string. Can be "spike_and_slab"
+        vs_prior_type: An optional string. Can be "spike_and_slab"
                               or "horseshoe" or "normal
-        :param vs_hyperparams: An optional dictionary of priors for the
+        vs_hyperparams: An optional dictionary of priors for the
                                variable selection hyperparameters
 
         """
@@ -705,16 +705,18 @@ def build_model(  # type: ignore
             # Create coefficient priors
             if vs_prior_type:
                 # Use variable selection priors
-                vs_prior_treatment = VariableSelectionPrior(
+                self.vs_prior_treatment = VariableSelectionPrior(
+                    vs_prior_type, vs_hyperparams
+                )
+                self.vs_prior_outcome = VariableSelectionPrior(
                     vs_prior_type, vs_hyperparams
                 )
-                vs_prior_outcome = VariableSelectionPrior(vs_prior_type, vs_hyperparams)
 
-                beta_t = vs_prior_treatment.create_prior(
+                beta_t = self.vs_prior_treatment.create_prior(
                     name="beta_t", n_params=Z.shape[1], dims="instruments", X=Z
                 )
 
-                beta_z = vs_prior_outcome.create_prior(
+                beta_z = self.vs_prior_outcome.create_prior(
                     name="beta_z", n_params=X.shape[1], dims="covariates", X=X
                 )
             else:
@@ -733,7 +735,7 @@ def build_model(  # type: ignore
                 )
 
             sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2)
-            chol, corr, sigmas = pm.LKJCholeskyCov(
+            chol, _, _ = pm.LKJCholeskyCov(
                 name="chol_cov",
                 eta=priors["eta"],
                 n=2,
diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py
index e3a1a1b9..efeb6bf9 100644
--- a/causalpy/tests/test_integration_pymc_examples.py
+++ b/causalpy/tests/test_integration_pymc_examples.py
@@ -706,6 +706,45 @@ def test_iv_reg_vs_prior(mock_pymc_sample):
         result.get_plot_data()
     assert "gamma_beta_t" in result.model.named_vars
     assert "pi_beta_t" in result.model.named_vars
+    summary = result.model.vs_prior_outcome.get_inclusion_probabilities(
+        result.idata, "beta_z"
+    )
+    assert isinstance(summary, pd.DataFrame)
+
+
+@pytest.mark.integration
+def test_iv_reg_vs_prior_hs(mock_pymc_sample):
+    df = cp.load_data("risk")
+    instruments_formula = "risk  ~ 1 + logmort0"
+    formula = "loggdp ~  1 + risk"
+    instruments_data = df[["risk", "logmort0"]]
+    data = df[["loggdp", "risk"]]
+
+    result = cp.InstrumentalVariable(
+        instruments_data=instruments_data,
+        data=data,
+        instruments_formula=instruments_formula,
+        formula=formula,
+        model=cp.pymc_models.InstrumentalVariableRegression(
+            sample_kwargs=sample_kwargs
+        ),
+        vs_prior_type="horseshoe",
+    )
+    result.model.sample_predictive_distribution(ppc_sampler="pymc")
+    assert isinstance(df, pd.DataFrame)
+    assert isinstance(data, pd.DataFrame)
+    assert isinstance(instruments_data, pd.DataFrame)
+    assert isinstance(result, cp.InstrumentalVariable)
+    assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"]
+    assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
+    with pytest.raises(NotImplementedError):
+        result.get_plot_data()
+    assert "tau_beta_t" in result.model.named_vars
+    assert "tau_beta_z" in result.model.named_vars
+    summary = result.model.vs_prior_outcome.get_shrinkage_factors(
+        result.idata, "beta_z"
+    )
+    assert isinstance(summary, pd.DataFrame)
 
 
 @pytest.mark.integration
diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index fdac7ff4..8c8b2001 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -23,6 +23,7 @@
 from typing import Any, Dict, Optional, Union
 
 import numpy as np
+import pandas as pd
 import pymc as pm
 import pytensor.tensor as pt
 from pymc_extras.prior import Prior
@@ -65,9 +66,10 @@ class SpikeAndSlabPrior:
     Creates a mixture prior with a point mass at zero (spike) and a diffuse
     normal distribution (slab), implemented as:
 
-    β_j = γ_j × β_j^raw
-
-    where γ_j ∈ [0,1] is a relaxed indicator and β_j^raw ~ N(0, σ_slab²).
+    .. math::
+        \beta_{j} = \gamma_{j} \cdot \beta_{j}^{\text{raw}} \\
+        \beta_{j}^{\text{raw}} \sim \mathcal{N}(0, \sigma_{\text{slab}}^{2}), \qquad
+        \gamma_{j} \in [0,1].
 
     Parameters
     ----------
@@ -145,9 +147,9 @@ class HorseshoePrior:
     Provides continuous shrinkage with heavy tails, allowing strong signals
     to escape shrinkage while weak signals are dampened:
 
-    β_j = τ · λ̃_j · β_j^raw
-
-    where λ̃_j = √(c²λ_j² / (c² + τ²λ_j²)) is the regularized local shrinkage.
+    .. math::
+        \beta_{j} & =  \tau \cdot \lambda_{j} \cdot \beta_{j}^{raw}  \\
+        \lambda_{j} & = \sqrt{ \dfrac{c^{2}\lambda_{j}^{2}}{c^{2} + \tau^{2}\lambda_{j}^{2}} }
 
     Parameters
     ----------
@@ -423,7 +425,7 @@ def create_prior(
 
     def get_inclusion_probabilities(
         self, idata, param_name: str, threshold: float = 0.5
-    ) -> Dict[str, np.ndarray]:
+    ) -> pd.DataFrame:
         """
         Extract variable inclusion probabilities from fitted model.
 
@@ -472,17 +474,24 @@ def get_inclusion_probabilities(
         gamma = az.extract(idata.posterior[gamma_name])
 
         # Compute inclusion probabilities
-        probabilities = (gamma > threshold).mean(dim="sample").values
-        gamma_mean = gamma.mean(dim="sample").values
+        probabilities = (gamma > threshold).mean(dim="sample").to_array()
+        gamma_mean = gamma.mean(dim="sample").to_array()
         selected = probabilities > threshold
 
-        return {
+        summary = {
             "probabilities": probabilities,
             "selected": selected,
             "gamma_mean": gamma_mean,
         }
+        probs = summary["probabilities"].T
+        df = pd.DataFrame(index=list(range(len(probs))))
+
+        df["prob"] = probs
+        df["selected"] = summary["selected"].T
+        df["gamma_mean"] = summary["gamma_mean"].T
+        return df
 
-    def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]:
+    def get_shrinkage_factors(self, idata, param_name: str) -> pd.DataFrame:
         """
         Extract shrinkage factors from horseshoe prior.
 
@@ -524,17 +533,26 @@ def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]
             raise ValueError(f"Could not find '{lambda_tilde_name}' in posterior")
 
         # Extract components
-        tau = az.extract(idata.posterior[tau_name])
-        lambda_tilde = az.extract(idata.posterior[lambda_tilde_name])
+        tau = az.extract(idata.posterior[tau_name]).to_array()
+        lambda_tilde = az.extract(idata.posterior[lambda_tilde_name]).to_array()
 
-        # Compute shrinkage factors
-        shrinkage_factors = (tau * lambda_tilde).mean(dim="sample").values
+        shrinkage_factor = np.array(
+            [tau[0, i] * lambda_tilde[0, :, :] for i in range(len(tau))]
+        )
+        shrinkage_factor = shrinkage_factor.mean(axis=2)
 
-        return {
-            "shrinkage_factors": shrinkage_factors,
-            "tau": tau.mean().values,
-            "lambda_tilde": lambda_tilde.mean(dim="sample").values,
+        summary = {
+            "shrinkage_factors": shrinkage_factor,
+            "tau": tau.mean(),
+            "lambda_tilde": lambda_tilde.mean(dim=("sample")),
         }
+        probs = summary["shrinkage_factors"].T
+        df = pd.DataFrame(index=list(range(len(probs))))
+        df["shrinkage_factor"] = probs
+
+        df["lambda_tilde"] = summary["lambda_tilde"].T
+        df["tau"] = np.mean(tau).item()
+        return df
 
 
 def create_variable_selection_prior(
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index a625f245..f135fc49 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,10 +1,10 @@
 
-    interrogate: 95.2%
+    interrogate: 95.0%
     
         
             
         
-        
+        
         
             
         
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        95.2%
-        95.2%
+        95.0%
+        95.0%
     
     
         
diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 6823d382..97e221ad 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2,22 +2,35 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 129,
    "id": "532c6736",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import pymc as pm\n",
+    "\n",
+    "import causalpy as cp\n",
     "\n",
-    "import causalpy as cp"
+    "%load_ext autoreload\n",
+    "%autoreload 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "046aa8e0",
    "metadata": {},
    "outputs": [
@@ -68,123 +81,123 @@
        "  \n",
        "    
\n",
        "      0\n",
-       "      -3.622930\n",
-       "      -4.426916\n",
-       "      0.267995\n",
-       "      0\n",
-       "      7.231021\n",
-       "      0.492408\n",
-       "      -0.153387\n",
-       "      0.632238\n",
-       "      1.549490\n",
-       "      -0.217191\n",
+       "      17.285021\n",
+       "      5.560022\n",
+       "      4.908333\n",
+       "      1\n",
+       "      4.221970\n",
+       "      -0.711212\n",
+       "      0.678770\n",
+       "      0.807216\n",
+       "      1.502488\n",
+       "      -0.194990\n",
        "      ...\n",
-       "      -0.483356\n",
-       "      0.541115\n",
-       "      -0.438765\n",
-       "      -0.419556\n",
-       "      0.237334\n",
-       "      -1.976732\n",
-       "      -0.320051\n",
-       "      -1.506112\n",
-       "      0.089369\n",
-       "      -0.985505\n",
+       "      -0.355420\n",
+       "      1.402186\n",
+       "      -1.517787\n",
+       "      0.292296\n",
+       "      -0.439312\n",
+       "      -0.400863\n",
+       "      1.478663\n",
+       "      1.030321\n",
+       "      1.535974\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      1\n",
-       "      -7.762899\n",
-       "      -1.634123\n",
-       "      -2.042926\n",
+       "      -5.418737\n",
+       "      -1.169369\n",
+       "      -1.416456\n",
        "      0\n",
-       "      1.650064\n",
-       "      -1.739974\n",
-       "      -0.574608\n",
-       "      -1.009005\n",
-       "      0.327720\n",
-       "      -0.879195\n",
+       "      3.571828\n",
+       "      -0.971269\n",
+       "      0.155694\n",
+       "      0.094208\n",
+       "      1.060349\n",
+       "      0.918542\n",
        "      ...\n",
-       "      0.055404\n",
-       "      -1.411090\n",
-       "      -0.518275\n",
-       "      0.152670\n",
-       "      -1.120139\n",
-       "      0.360184\n",
-       "      -0.686483\n",
-       "      -0.795379\n",
-       "      -0.662693\n",
-       "      -0.985505\n",
+       "      0.647043\n",
+       "      0.978512\n",
+       "      0.102692\n",
+       "      -1.338446\n",
+       "      1.164631\n",
+       "      1.157542\n",
+       "      -0.477164\n",
+       "      -0.677874\n",
+       "      -0.459080\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      2\n",
-       "      11.977784\n",
-       "      5.799723\n",
-       "      3.059354\n",
+       "      8.329916\n",
+       "      5.750195\n",
+       "      1.859907\n",
        "      1\n",
-       "      4.474363\n",
-       "      -0.610255\n",
-       "      -0.305802\n",
-       "      -0.602653\n",
-       "      1.882276\n",
-       "      -0.000420\n",
+       "      6.389524\n",
+       "      0.155810\n",
+       "      0.097990\n",
+       "      1.886364\n",
+       "      0.204515\n",
+       "      1.407368\n",
        "      ...\n",
-       "      0.197202\n",
-       "      -1.941906\n",
-       "      -1.287992\n",
-       "      0.237949\n",
-       "      0.721191\n",
-       "      0.151575\n",
-       "      1.060779\n",
-       "      1.096449\n",
-       "      0.997784\n",
-       "      1.014302\n",
+       "      -0.789757\n",
+       "      -1.376844\n",
+       "      -1.164524\n",
+       "      -0.854488\n",
+       "      -0.563027\n",
+       "      -1.762406\n",
+       "      0.707221\n",
+       "      1.078595\n",
+       "      0.574396\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      3\n",
-       "      -5.484061\n",
-       "      1.275083\n",
-       "      -2.253048\n",
+       "      -14.109005\n",
+       "      -3.260798\n",
+       "      -3.616069\n",
        "      0\n",
-       "      5.692706\n",
-       "      -0.122917\n",
-       "      -0.315150\n",
-       "      -1.469290\n",
-       "      -0.717482\n",
-       "      -0.439688\n",
+       "      1.522008\n",
+       "      -1.791197\n",
+       "      -0.025364\n",
+       "      -1.849614\n",
+       "      -0.420513\n",
+       "      -0.813558\n",
        "      ...\n",
-       "      0.312800\n",
-       "      -0.379406\n",
-       "      -0.648433\n",
-       "      0.649580\n",
-       "      1.006272\n",
-       "      0.927402\n",
-       "      -0.484782\n",
-       "      -0.055020\n",
-       "      -0.731075\n",
-       "      -0.985505\n",
+       "      -0.967529\n",
+       "      0.604905\n",
+       "      0.136397\n",
+       "      0.087200\n",
+       "      -0.575056\n",
+       "      -0.244472\n",
+       "      -1.225791\n",
+       "      -1.208765\n",
+       "      -1.152913\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      4\n",
-       "      -12.907767\n",
-       "      -2.415022\n",
-       "      -3.497581\n",
-       "      0\n",
-       "      3.875030\n",
-       "      -0.849988\n",
-       "      -0.323205\n",
-       "      0.319486\n",
-       "      0.996123\n",
-       "      -0.457897\n",
+       "      15.927794\n",
+       "      6.070905\n",
+       "      4.285630\n",
+       "      1\n",
+       "      8.262360\n",
+       "      0.904944\n",
+       "      -0.648125\n",
+       "      -0.420076\n",
+       "      0.774960\n",
+       "      0.612525\n",
        "      ...\n",
-       "      0.802840\n",
-       "      1.348549\n",
-       "      -0.054392\n",
-       "      1.038429\n",
-       "      0.353959\n",
-       "      -0.682012\n",
-       "      -1.141859\n",
-       "      -0.994109\n",
-       "      -1.136093\n",
-       "      -0.985505\n",
+       "      2.362694\n",
+       "      2.702110\n",
+       "      -1.565191\n",
+       "      1.689404\n",
+       "      1.198610\n",
+       "      0.117392\n",
+       "      1.361744\n",
+       "      1.160004\n",
+       "      1.339552\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      ...\n",
@@ -212,123 +225,123 @@
        "    
\n",
        "    
\n",
        "      2495\n",
-       "      -10.076508\n",
-       "      -5.832192\n",
-       "      -1.414772\n",
-       "      0\n",
-       "      9.270431\n",
-       "      1.308172\n",
-       "      -0.802954\n",
-       "      -2.487989\n",
-       "      1.100350\n",
-       "      -1.214653\n",
+       "      -6.973181\n",
+       "      -2.492475\n",
+       "      -0.493568\n",
+       "      1\n",
+       "      6.399022\n",
+       "      0.159609\n",
+       "      2.004918\n",
+       "      1.850936\n",
+       "      -0.205821\n",
+       "      1.826866\n",
        "      ...\n",
-       "      -1.632467\n",
-       "      -0.454553\n",
-       "      -2.521362\n",
-       "      -1.285181\n",
-       "      0.121284\n",
-       "      -0.113719\n",
-       "      -0.891262\n",
-       "      -1.863739\n",
-       "      -0.458268\n",
-       "      -0.985505\n",
+       "      0.180511\n",
+       "      -0.656888\n",
+       "      -1.253321\n",
+       "      1.057077\n",
+       "      -0.803677\n",
+       "      -0.759486\n",
+       "      -0.611072\n",
+       "      -1.013733\n",
+       "      -0.167970\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      2496\n",
-       "      -1.382889\n",
-       "      3.700458\n",
-       "      -0.694449\n",
-       "      1\n",
-       "      6.743524\n",
-       "      0.297409\n",
-       "      0.430215\n",
-       "      -1.670237\n",
-       "      -1.288557\n",
-       "      0.760799\n",
+       "      -7.457602\n",
+       "      1.094568\n",
+       "      -2.850723\n",
+       "      0\n",
+       "      9.043466\n",
+       "      1.217386\n",
+       "      -1.788360\n",
+       "      -0.342299\n",
+       "      -0.635556\n",
+       "      0.595195\n",
        "      ...\n",
-       "      0.797380\n",
-       "      0.238452\n",
-       "      -0.227972\n",
-       "      -0.593133\n",
-       "      1.936234\n",
-       "      0.074842\n",
-       "      -0.121784\n",
-       "      0.562210\n",
-       "      -0.223847\n",
-       "      1.014302\n",
+       "      -0.020930\n",
+       "      -0.459529\n",
+       "      1.528013\n",
+       "      0.135980\n",
+       "      -0.448317\n",
+       "      -0.113937\n",
+       "      -0.652803\n",
+       "      -0.103194\n",
+       "      -0.911497\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      2497\n",
-       "      15.394511\n",
-       "      4.097691\n",
-       "      4.765607\n",
-       "      1\n",
-       "      6.960287\n",
-       "      0.384115\n",
-       "      0.306140\n",
-       "      -0.810919\n",
-       "      0.061464\n",
-       "      -0.303729\n",
+       "      -12.389506\n",
+       "      -5.563091\n",
+       "      -2.275472\n",
+       "      0\n",
+       "      2.631578\n",
+       "      -1.347369\n",
+       "      0.948935\n",
+       "      0.733226\n",
+       "      0.776689\n",
+       "      1.007780\n",
        "      ...\n",
-       "      -1.221077\n",
-       "      0.501586\n",
-       "      -0.505733\n",
-       "      -0.299768\n",
-       "      -1.878130\n",
-       "      0.959871\n",
-       "      1.363197\n",
-       "      0.663301\n",
-       "      1.553063\n",
-       "      1.014302\n",
+       "      0.394645\n",
+       "      -1.223821\n",
+       "      -0.281657\n",
+       "      -0.934772\n",
+       "      -0.587933\n",
+       "      1.399632\n",
+       "      -1.077664\n",
+       "      -1.793181\n",
+       "      -0.730043\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      2498\n",
-       "      -1.563496\n",
-       "      1.610554\n",
-       "      -0.058017\n",
+       "      4.181542\n",
+       "      1.763852\n",
+       "      1.805896\n",
        "      1\n",
-       "      7.299606\n",
-       "      0.519842\n",
-       "      0.687352\n",
-       "      0.374898\n",
-       "      -1.522148\n",
-       "      0.237806\n",
+       "      6.074689\n",
+       "      0.029876\n",
+       "      1.265076\n",
+       "      -1.047612\n",
+       "      -0.174857\n",
+       "      -1.611537\n",
        "      ...\n",
-       "      1.651592\n",
-       "      1.484596\n",
-       "      0.627287\n",
-       "      0.012603\n",
-       "      -0.208517\n",
-       "      -0.946828\n",
-       "      -0.137769\n",
-       "      0.030354\n",
-       "      -0.016728\n",
-       "      1.014302\n",
+       "      0.660882\n",
+       "      1.338977\n",
+       "      -1.475265\n",
+       "      -1.475989\n",
+       "      0.910175\n",
+       "      0.325985\n",
+       "      0.349857\n",
+       "      0.066698\n",
+       "      0.557359\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      2499\n",
-       "      -12.175734\n",
-       "      -0.847090\n",
-       "      -2.776214\n",
+       "      3.783388\n",
+       "      4.814916\n",
+       "      0.656157\n",
        "      1\n",
-       "      5.305630\n",
-       "      -0.277748\n",
-       "      0.886482\n",
-       "      1.794044\n",
-       "      -0.815157\n",
-       "      -1.947044\n",
+       "      3.824017\n",
+       "      -0.870393\n",
+       "      -0.000393\n",
+       "      -0.373005\n",
+       "      -1.857066\n",
+       "      0.327473\n",
        "      ...\n",
-       "      1.301984\n",
-       "      1.278139\n",
-       "      -0.817968\n",
-       "      -0.772485\n",
-       "      -1.288553\n",
-       "      -0.702799\n",
-       "      -1.077067\n",
-       "      -0.595088\n",
-       "      -0.901333\n",
-       "      1.014302\n",
+       "      0.725508\n",
+       "      -1.219133\n",
+       "      -2.110389\n",
+       "      -0.375057\n",
+       "      0.280377\n",
+       "      -0.899183\n",
+       "      0.315558\n",
+       "      0.841183\n",
+       "      0.194693\n",
+       "      1.001401\n",
        "    
\n",
        "  \n",
        "\n",
@@ -337,61 +350,61 @@
       ],
       "text/plain": [
        "         Y_cont     Y_bin    T_cont  T_bin     alpha  feature_0  feature_1  \\\n",
-       "0     -3.622930 -4.426916  0.267995      0  7.231021   0.492408  -0.153387   \n",
-       "1     -7.762899 -1.634123 -2.042926      0  1.650064  -1.739974  -0.574608   \n",
-       "2     11.977784  5.799723  3.059354      1  4.474363  -0.610255  -0.305802   \n",
-       "3     -5.484061  1.275083 -2.253048      0  5.692706  -0.122917  -0.315150   \n",
-       "4    -12.907767 -2.415022 -3.497581      0  3.875030  -0.849988  -0.323205   \n",
+       "0     17.285021  5.560022  4.908333      1  4.221970  -0.711212   0.678770   \n",
+       "1     -5.418737 -1.169369 -1.416456      0  3.571828  -0.971269   0.155694   \n",
+       "2      8.329916  5.750195  1.859907      1  6.389524   0.155810   0.097990   \n",
+       "3    -14.109005 -3.260798 -3.616069      0  1.522008  -1.791197  -0.025364   \n",
+       "4     15.927794  6.070905  4.285630      1  8.262360   0.904944  -0.648125   \n",
        "...         ...       ...       ...    ...       ...        ...        ...   \n",
-       "2495 -10.076508 -5.832192 -1.414772      0  9.270431   1.308172  -0.802954   \n",
-       "2496  -1.382889  3.700458 -0.694449      1  6.743524   0.297409   0.430215   \n",
-       "2497  15.394511  4.097691  4.765607      1  6.960287   0.384115   0.306140   \n",
-       "2498  -1.563496  1.610554 -0.058017      1  7.299606   0.519842   0.687352   \n",
-       "2499 -12.175734 -0.847090 -2.776214      1  5.305630  -0.277748   0.886482   \n",
+       "2495  -6.973181 -2.492475 -0.493568      1  6.399022   0.159609   2.004918   \n",
+       "2496  -7.457602  1.094568 -2.850723      0  9.043466   1.217386  -1.788360   \n",
+       "2497 -12.389506 -5.563091 -2.275472      0  2.631578  -1.347369   0.948935   \n",
+       "2498   4.181542  1.763852  1.805896      1  6.074689   0.029876   1.265076   \n",
+       "2499   3.783388  4.814916  0.656157      1  3.824017  -0.870393  -0.000393   \n",
        "\n",
        "      feature_2  feature_3  feature_4  ...  feature_8  feature_9  feature_10  \\\n",
-       "0      0.632238   1.549490  -0.217191  ...  -0.483356   0.541115   -0.438765   \n",
-       "1     -1.009005   0.327720  -0.879195  ...   0.055404  -1.411090   -0.518275   \n",
-       "2     -0.602653   1.882276  -0.000420  ...   0.197202  -1.941906   -1.287992   \n",
-       "3     -1.469290  -0.717482  -0.439688  ...   0.312800  -0.379406   -0.648433   \n",
-       "4      0.319486   0.996123  -0.457897  ...   0.802840   1.348549   -0.054392   \n",
+       "0      0.807216   1.502488  -0.194990  ...  -0.355420   1.402186   -1.517787   \n",
+       "1      0.094208   1.060349   0.918542  ...   0.647043   0.978512    0.102692   \n",
+       "2      1.886364   0.204515   1.407368  ...  -0.789757  -1.376844   -1.164524   \n",
+       "3     -1.849614  -0.420513  -0.813558  ...  -0.967529   0.604905    0.136397   \n",
+       "4     -0.420076   0.774960   0.612525  ...   2.362694   2.702110   -1.565191   \n",
        "...         ...        ...        ...  ...        ...        ...         ...   \n",
-       "2495  -2.487989   1.100350  -1.214653  ...  -1.632467  -0.454553   -2.521362   \n",
-       "2496  -1.670237  -1.288557   0.760799  ...   0.797380   0.238452   -0.227972   \n",
-       "2497  -0.810919   0.061464  -0.303729  ...  -1.221077   0.501586   -0.505733   \n",
-       "2498   0.374898  -1.522148   0.237806  ...   1.651592   1.484596    0.627287   \n",
-       "2499   1.794044  -0.815157  -1.947044  ...   1.301984   1.278139   -0.817968   \n",
+       "2495   1.850936  -0.205821   1.826866  ...   0.180511  -0.656888   -1.253321   \n",
+       "2496  -0.342299  -0.635556   0.595195  ...  -0.020930  -0.459529    1.528013   \n",
+       "2497   0.733226   0.776689   1.007780  ...   0.394645  -1.223821   -0.281657   \n",
+       "2498  -1.047612  -0.174857  -1.611537  ...   0.660882   1.338977   -1.475265   \n",
+       "2499  -0.373005  -1.857066   0.327473  ...   0.725508  -1.219133   -2.110389   \n",
        "\n",
        "      feature_11  feature_12  feature_13  Y_cont_scaled  Y_bin_scaled  \\\n",
-       "0      -0.419556    0.237334   -1.976732      -0.320051     -1.506112   \n",
-       "1       0.152670   -1.120139    0.360184      -0.686483     -0.795379   \n",
-       "2       0.237949    0.721191    0.151575       1.060779      1.096449   \n",
-       "3       0.649580    1.006272    0.927402      -0.484782     -0.055020   \n",
-       "4       1.038429    0.353959   -0.682012      -1.141859     -0.994109   \n",
+       "0       0.292296   -0.439312   -0.400863       1.478663      1.030321   \n",
+       "1      -1.338446    1.164631    1.157542      -0.477164     -0.677874   \n",
+       "2      -0.854488   -0.563027   -1.762406       0.707221      1.078595   \n",
+       "3       0.087200   -0.575056   -0.244472      -1.225791     -1.208765   \n",
+       "4       1.689404    1.198610    0.117392       1.361744      1.160004   \n",
        "...          ...         ...         ...            ...           ...   \n",
-       "2495   -1.285181    0.121284   -0.113719      -0.891262     -1.863739   \n",
-       "2496   -0.593133    1.936234    0.074842      -0.121784      0.562210   \n",
-       "2497   -0.299768   -1.878130    0.959871       1.363197      0.663301   \n",
-       "2498    0.012603   -0.208517   -0.946828      -0.137769      0.030354   \n",
-       "2499   -0.772485   -1.288553   -0.702799      -1.077067     -0.595088   \n",
+       "2495    1.057077   -0.803677   -0.759486      -0.611072     -1.013733   \n",
+       "2496    0.135980   -0.448317   -0.113937      -0.652803     -0.103194   \n",
+       "2497   -0.934772   -0.587933    1.399632      -1.077664     -1.793181   \n",
+       "2498   -1.475989    0.910175    0.325985       0.349857      0.066698   \n",
+       "2499   -0.375057    0.280377   -0.899183       0.315558      0.841183   \n",
        "\n",
        "      T_cont_scaled  T_bin_scaled  \n",
-       "0          0.089369     -0.985505  \n",
-       "1         -0.662693     -0.985505  \n",
-       "2          0.997784      1.014302  \n",
-       "3         -0.731075     -0.985505  \n",
-       "4         -1.136093     -0.985505  \n",
+       "0          1.535974      1.001401  \n",
+       "1         -0.459080     -0.998202  \n",
+       "2          0.574396      1.001401  \n",
+       "3         -1.152913     -0.998202  \n",
+       "4          1.339552      1.001401  \n",
        "...             ...           ...  \n",
-       "2495      -0.458268     -0.985505  \n",
-       "2496      -0.223847      1.014302  \n",
-       "2497       1.553063      1.014302  \n",
-       "2498      -0.016728      1.014302  \n",
-       "2499      -0.901333      1.014302  \n",
+       "2495      -0.167970      1.001401  \n",
+       "2496      -0.911497     -0.998202  \n",
+       "2497      -0.730043     -0.998202  \n",
+       "2498       0.557359      1.001401  \n",
+       "2499       0.194693      1.001401  \n",
        "\n",
        "[2500 rows x 23 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -481,9 +494,42 @@
     "data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "806df6ea",
+   "metadata": {},
+   "source": [
+    "### Hyperparameters for Variable Selection Priors"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "id": "ae848fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "axs[0].hist(pm.draw(pm.Beta.dist(2, 2), 1000), ec=\"black\", color=\"slateblue\")\n",
+    "axs[1].hist(pm.draw(pm.Beta.dist(2, 5), 1000), ec=\"black\", color=\"slateblue\")\n",
+    "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "763ca253",
    "metadata": {},
    "outputs": [
@@ -501,11 +547,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
       "                This is not necessarily a problem but it violates\n",
       "                the assumption of a simple IV experiment.\n",
       "                The coefficients should be interpreted appropriately.\n",
-      "  \"\"\"Validate the input data and model formula for correctness\"\"\"\n",
+      "  warnings.warn(\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
       "NUTS: [beta_t, beta_z, chol_cov]\n",
@@ -516,7 +562,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75ceaa4f49be4429bdb3671caae6d44f",
+       "model_id": "6de2845a3ae7405c85006a8c991e5614",
        "version_major": 2,
        "version_minor": 0
       },
@@ -531,8 +577,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
      ]
@@ -551,16 +595,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 69 seconds.\n",
-      "There were 17 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 99 seconds.\n",
       "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
-      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
       "                This is not necessarily a problem but it violates\n",
       "                the assumption of a simple IV experiment.\n",
       "                The coefficients should be interpreted appropriately.\n",
-      "  \"\"\"Validate the input data and model formula for correctness\"\"\"\n"
+      "  warnings.warn(\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "  warnings.warn(\n"
      ]
     },
     {
@@ -587,7 +631,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8478b37ed9ea450c9d18f31ceacc9c16",
+       "model_id": "b1fa39df9f8f40b4ae84c1d4783720c9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -602,6 +646,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
      ]
@@ -620,11 +666,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 316 seconds.\n",
-      "There were 86 divergences after tuning. Increase `target_accept` or reparameterize.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
-      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
-      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 461 seconds.\n",
+      "There were 79 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
      ]
     }
    ],
@@ -634,7 +678,7 @@
     "    \"tune\": 2000,\n",
     "    \"chains\": 2,\n",
     "    \"cores\": 2,\n",
-    "    \"mptarget_accept\": 0.95,\n",
+    "    \"target_accept\": 0.95,\n",
     "    \"progressbar\": True,\n",
     "    \"random_seed\": 42,\n",
     "    \"mp_ctx\": \"spawn\",\n",
@@ -680,8 +724,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "a9633162",
+   "execution_count": 7,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_spike_slab.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "127888b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")\n",
+    "axs[0].set_title(\"Parameter Comparison Outcome Model \\n Baseline v Spike and Slab\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09b24bf",
+   "metadata": {},
+   "source": [
+    "#### The Treatment Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "acafc928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_t\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")\n",
+    "\n",
+    "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a0b213",
    "metadata": {},
    "outputs": [
     {
@@ -705,265 +853,254 @@
        "  \n",
        "    
\n",
        "      \n",
-       "      mean\n",
-       "      sd\n",
-       "      hdi_3%\n",
-       "      hdi_97%\n",
-       "      mcse_mean\n",
-       "      mcse_sd\n",
-       "      ess_bulk\n",
-       "      ess_tail\n",
-       "      r_hat\n",
+       "      prob\n",
+       "      selected\n",
+       "      gamma_mean\n",
        "    
\n",
        "  \n",
        "  \n",
        "    
\n",
-       "      beta_z[Intercept]\n",
-       "      0.015\n",
-       "      0.059\n",
-       "      -0.098\n",
-       "      0.125\n",
-       "      0.001\n",
-       "      0.002\n",
-       "      1658.0\n",
-       "      1023.0\n",
-       "      1.00\n",
+       "      0\n",
+       "      0.0105\n",
+       "      False\n",
+       "      0.014042\n",
        "    
\n",
        "    
\n",
-       "      beta_z[T_cont]\n",
-       "      3.154\n",
-       "      0.357\n",
-       "      2.429\n",
-       "      3.777\n",
-       "      0.029\n",
-       "      0.011\n",
-       "      152.0\n",
-       "      373.0\n",
-       "      1.01\n",
+       "      1\n",
+       "      1.0000\n",
+       "      True\n",
+       "      0.990424\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_0]\n",
-       "      0.430\n",
-       "      0.233\n",
-       "      0.017\n",
-       "      0.905\n",
-       "      0.019\n",
-       "      0.007\n",
-       "      159.0\n",
-       "      436.0\n",
-       "      1.01\n",
+       "      2\n",
+       "      0.7605\n",
+       "      True\n",
+       "      0.775804\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_1]\n",
-       "      -0.383\n",
-       "      0.059\n",
-       "      -0.491\n",
-       "      -0.277\n",
-       "      0.001\n",
-       "      0.002\n",
-       "      1720.0\n",
-       "      1250.0\n",
-       "      1.00\n",
+       "      3\n",
+       "      0.6880\n",
+       "      True\n",
+       "      0.719690\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_2]\n",
-       "      0.030\n",
-       "      0.162\n",
-       "      -0.272\n",
-       "      0.341\n",
-       "      0.012\n",
-       "      0.005\n",
-       "      172.0\n",
-       "      418.0\n",
-       "      1.01\n",
+       "      4\n",
+       "      0.0570\n",
+       "      False\n",
+       "      0.064343\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_3]\n",
-       "      -0.055\n",
-       "      0.143\n",
-       "      -0.301\n",
-       "      0.242\n",
-       "      0.011\n",
-       "      0.005\n",
-       "      171.0\n",
-       "      428.0\n",
-       "      1.00\n",
+       "      5\n",
+       "      0.0330\n",
+       "      False\n",
+       "      0.038359\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_4]\n",
-       "      0.442\n",
-       "      0.064\n",
-       "      0.335\n",
-       "      0.564\n",
-       "      0.003\n",
-       "      0.004\n",
-       "      685.0\n",
-       "      668.0\n",
-       "      1.00\n",
+       "      6\n",
+       "      0.6755\n",
+       "      True\n",
+       "      0.704165\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_5]\n",
-       "      -0.000\n",
-       "      0.058\n",
-       "      -0.101\n",
-       "      0.113\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      1338.0\n",
-       "      970.0\n",
-       "      1.00\n",
+       "      7\n",
+       "      0.0085\n",
+       "      False\n",
+       "      0.011460\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_6]\n",
-       "      0.027\n",
-       "      0.061\n",
-       "      -0.094\n",
-       "      0.140\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      1184.0\n",
-       "      1063.0\n",
-       "      1.00\n",
+       "      8\n",
+       "      0.0170\n",
+       "      False\n",
+       "      0.021624\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_7]\n",
-       "      -0.037\n",
-       "      0.058\n",
-       "      -0.140\n",
-       "      0.077\n",
-       "      0.002\n",
-       "      0.001\n",
-       "      914.0\n",
-       "      675.0\n",
-       "      1.00\n",
+       "      9\n",
+       "      0.0130\n",
+       "      False\n",
+       "      0.015590\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_8]\n",
-       "      -0.076\n",
-       "      0.060\n",
-       "      -0.193\n",
-       "      0.034\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      827.0\n",
-       "      602.0\n",
-       "      1.00\n",
+       "      10\n",
+       "      0.0080\n",
+       "      False\n",
+       "      0.010675\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_9]\n",
-       "      0.042\n",
-       "      0.060\n",
-       "      -0.072\n",
-       "      0.146\n",
-       "      0.002\n",
-       "      0.004\n",
-       "      1191.0\n",
-       "      809.0\n",
-       "      1.00\n",
+       "      11\n",
+       "      0.0250\n",
+       "      False\n",
+       "      0.029619\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_10]\n",
-       "      -0.034\n",
-       "      0.061\n",
-       "      -0.151\n",
-       "      0.077\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      776.0\n",
-       "      907.0\n",
-       "      1.00\n",
+       "      12\n",
+       "      0.0060\n",
+       "      False\n",
+       "      0.008689\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_11]\n",
-       "      0.075\n",
-       "      0.059\n",
-       "      -0.029\n",
-       "      0.190\n",
-       "      0.002\n",
-       "      0.001\n",
-       "      864.0\n",
-       "      904.0\n",
-       "      1.00\n",
+       "      13\n",
+       "      0.0250\n",
+       "      False\n",
+       "      0.028057\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_12]\n",
-       "      -0.045\n",
-       "      0.061\n",
-       "      -0.160\n",
-       "      0.064\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      951.0\n",
-       "      946.0\n",
-       "      1.00\n",
+       "      14\n",
+       "      0.0135\n",
+       "      False\n",
+       "      0.016080\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_13]\n",
-       "      -0.040\n",
-       "      0.059\n",
-       "      -0.164\n",
-       "      0.060\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      1466.0\n",
-       "      1266.0\n",
-       "      1.00\n",
+       "      15\n",
+       "      0.0135\n",
+       "      False\n",
+       "      0.015825\n",
        "    
\n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "beta_z[Intercept]   0.015  0.059  -0.098    0.125      0.001    0.002   \n",
-       "beta_z[T_cont]      3.154  0.357   2.429    3.777      0.029    0.011   \n",
-       "beta_z[feature_0]   0.430  0.233   0.017    0.905      0.019    0.007   \n",
-       "beta_z[feature_1]  -0.383  0.059  -0.491   -0.277      0.001    0.002   \n",
-       "beta_z[feature_2]   0.030  0.162  -0.272    0.341      0.012    0.005   \n",
-       "beta_z[feature_3]  -0.055  0.143  -0.301    0.242      0.011    0.005   \n",
-       "beta_z[feature_4]   0.442  0.064   0.335    0.564      0.003    0.004   \n",
-       "beta_z[feature_5]  -0.000  0.058  -0.101    0.113      0.002    0.002   \n",
-       "beta_z[feature_6]   0.027  0.061  -0.094    0.140      0.002    0.002   \n",
-       "beta_z[feature_7]  -0.037  0.058  -0.140    0.077      0.002    0.001   \n",
-       "beta_z[feature_8]  -0.076  0.060  -0.193    0.034      0.002    0.002   \n",
-       "beta_z[feature_9]   0.042  0.060  -0.072    0.146      0.002    0.004   \n",
-       "beta_z[feature_10] -0.034  0.061  -0.151    0.077      0.002    0.002   \n",
-       "beta_z[feature_11]  0.075  0.059  -0.029    0.190      0.002    0.001   \n",
-       "beta_z[feature_12] -0.045  0.061  -0.160    0.064      0.002    0.002   \n",
-       "beta_z[feature_13] -0.040  0.059  -0.164    0.060      0.002    0.002   \n",
-       "\n",
-       "                    ess_bulk  ess_tail  r_hat  \n",
-       "beta_z[Intercept]     1658.0    1023.0   1.00  \n",
-       "beta_z[T_cont]         152.0     373.0   1.01  \n",
-       "beta_z[feature_0]      159.0     436.0   1.01  \n",
-       "beta_z[feature_1]     1720.0    1250.0   1.00  \n",
-       "beta_z[feature_2]      172.0     418.0   1.01  \n",
-       "beta_z[feature_3]      171.0     428.0   1.00  \n",
-       "beta_z[feature_4]      685.0     668.0   1.00  \n",
-       "beta_z[feature_5]     1338.0     970.0   1.00  \n",
-       "beta_z[feature_6]     1184.0    1063.0   1.00  \n",
-       "beta_z[feature_7]      914.0     675.0   1.00  \n",
-       "beta_z[feature_8]      827.0     602.0   1.00  \n",
-       "beta_z[feature_9]     1191.0     809.0   1.00  \n",
-       "beta_z[feature_10]     776.0     907.0   1.00  \n",
-       "beta_z[feature_11]     864.0     904.0   1.00  \n",
-       "beta_z[feature_12]     951.0     946.0   1.00  \n",
-       "beta_z[feature_13]    1466.0    1266.0   1.00  "
+       "      prob  selected  gamma_mean\n",
+       "0   0.0105     False    0.014042\n",
+       "1   1.0000      True    0.990424\n",
+       "2   0.7605      True    0.775804\n",
+       "3   0.6880      True    0.719690\n",
+       "4   0.0570     False    0.064343\n",
+       "5   0.0330     False    0.038359\n",
+       "6   0.6755      True    0.704165\n",
+       "7   0.0085     False    0.011460\n",
+       "8   0.0170     False    0.021624\n",
+       "9   0.0130     False    0.015590\n",
+       "10  0.0080     False    0.010675\n",
+       "11  0.0250     False    0.029619\n",
+       "12  0.0060     False    0.008689\n",
+       "13  0.0250     False    0.028057\n",
+       "14  0.0135     False    0.016080\n",
+       "15  0.0135     False    0.015825"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "az.summary(result_normal.idata, var_names=[\"beta_z\"])"
+    "summary = result_spike_slab.model.vs_prior_outcome.get_inclusion_probabilities(\n",
+    "    result_spike_slab.idata, \"beta_z\"\n",
+    ")\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38568d27",
+   "metadata": {},
+   "source": [
+    "## Horseshoe"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "bfcdeecd",
+   "execution_count": 72,
+   "id": "63edfa4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 3: Horseshoe Priors\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                This is not necessarily a problem but it violates\n",
+      "                the assumption of a simple IV experiment.\n",
+      "                The coefficients should be interpreted appropriately.\n",
+      "  warnings.warn(\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "  warnings.warn(\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "NUTS: [tau_beta_t, lambda_beta_t, c2_beta_t, beta_t_raw, tau_beta_z, lambda_beta_z, c2_beta_z, beta_z_raw, chol_cov]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1407969a4c4946d0920f0defc2ec153c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 743 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# =========================================================================\n",
+    "# Model 2: Horseshoe priors\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 3: Horseshoe Priors\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_horseshoe = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=\"horseshoe\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "9c283ee1",
    "metadata": {},
    "outputs": [
     {
@@ -987,312 +1124,165 @@
        "  \n",
        "    \n",
        "      \n",
-       "      mean\n",
-       "      sd\n",
-       "      hdi_3%\n",
-       "      hdi_97%\n",
-       "      mcse_mean\n",
-       "      mcse_sd\n",
-       "      ess_bulk\n",
-       "      ess_tail\n",
-       "      r_hat\n",
+       "      shrinkage_factor\n",
+       "      lambda_tilde\n",
+       "      tau\n",
        "    \n",
        "  \n",
        "  \n",
        "    \n",
-       "      beta_z[Intercept]\n",
-       "      0.001\n",
-       "      0.010\n",
-       "      -0.015\n",
-       "      0.011\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      1035.0\n",
-       "      807.0\n",
-       "      1.00\n",
+       "      0\n",
+       "      0.024168\n",
+       "      1.163944\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[T_cont]\n",
-       "      3.076\n",
-       "      0.099\n",
-       "      2.900\n",
-       "      3.269\n",
-       "      0.006\n",
-       "      0.004\n",
-       "      286.0\n",
-       "      340.0\n",
-       "      1.00\n",
+       "      1\n",
+       "      1.574053\n",
+       "      75.808614\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_0]\n",
-       "      0.476\n",
-       "      0.086\n",
-       "      0.323\n",
-       "      0.648\n",
-       "      0.004\n",
-       "      0.002\n",
-       "      477.0\n",
-       "      571.0\n",
-       "      1.00\n",
+       "      2\n",
+       "      0.454083\n",
+       "      21.869287\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_1]\n",
-       "      -0.392\n",
-       "      0.047\n",
-       "      -0.480\n",
-       "      -0.306\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      1903.0\n",
-       "      1459.0\n",
-       "      1.00\n",
+       "      3\n",
+       "      0.416588\n",
+       "      20.063451\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_2]\n",
-       "      -0.000\n",
-       "      0.018\n",
-       "      -0.023\n",
-       "      0.019\n",
-       "      0.001\n",
-       "      0.002\n",
-       "      464.0\n",
-       "      886.0\n",
-       "      1.01\n",
+       "      4\n",
+       "      0.078152\n",
+       "      3.763922\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_3]\n",
-       "      -0.003\n",
-       "      0.021\n",
-       "      -0.031\n",
-       "      0.018\n",
-       "      0.001\n",
-       "      0.003\n",
-       "      357.0\n",
-       "      313.0\n",
-       "      1.00\n",
+       "      5\n",
+       "      0.043892\n",
+       "      2.113912\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_4]\n",
-       "      0.423\n",
-       "      0.049\n",
-       "      0.341\n",
-       "      0.520\n",
-       "      0.003\n",
-       "      0.001\n",
-       "      380.0\n",
-       "      176.0\n",
-       "      1.01\n",
+       "      6\n",
+       "      0.388261\n",
+       "      18.699190\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_5]\n",
-       "      0.000\n",
-       "      0.009\n",
-       "      -0.012\n",
-       "      0.008\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      629.0\n",
-       "      1103.0\n",
-       "      1.00\n",
+       "      7\n",
+       "      0.027243\n",
+       "      1.312060\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_6]\n",
-       "      0.003\n",
-       "      0.016\n",
-       "      -0.012\n",
-       "      0.033\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      781.0\n",
-       "      1101.0\n",
-       "      1.00\n",
+       "      8\n",
+       "      0.027663\n",
+       "      1.332293\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_7]\n",
-       "      -0.005\n",
-       "      0.019\n",
-       "      -0.038\n",
-       "      0.015\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      738.0\n",
-       "      472.0\n",
-       "      1.01\n",
+       "      9\n",
+       "      0.026640\n",
+       "      1.283037\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_8]\n",
-       "      -0.004\n",
-       "      0.018\n",
-       "      -0.039\n",
-       "      0.012\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      700.0\n",
-       "      973.0\n",
-       "      1.00\n",
+       "      10\n",
+       "      0.026496\n",
+       "      1.276087\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_9]\n",
-       "      0.002\n",
-       "      0.014\n",
-       "      -0.012\n",
-       "      0.013\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      925.0\n",
-       "      1187.0\n",
-       "      1.00\n",
+       "      11\n",
+       "      0.031909\n",
+       "      1.536788\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_10]\n",
-       "      -0.001\n",
-       "      0.010\n",
-       "      -0.011\n",
-       "      0.012\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      1407.0\n",
-       "      1059.0\n",
-       "      1.00\n",
+       "      12\n",
+       "      0.024832\n",
+       "      1.195942\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_11]\n",
-       "      0.004\n",
-       "      0.019\n",
-       "      -0.016\n",
-       "      0.031\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      787.0\n",
-       "      727.0\n",
-       "      1.01\n",
+       "      13\n",
+       "      0.032776\n",
+       "      1.578531\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_12]\n",
-       "      -0.006\n",
-       "      0.023\n",
-       "      -0.050\n",
-       "      0.010\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      394.0\n",
-       "      684.0\n",
-       "      1.01\n",
+       "      14\n",
+       "      0.026933\n",
+       "      1.297110\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_13]\n",
-       "      -0.001\n",
-       "      0.011\n",
-       "      -0.012\n",
-       "      0.005\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      746.0\n",
-       "      1345.0\n",
-       "      1.00\n",
+       "      15\n",
+       "      0.024878\n",
+       "      1.198152\n",
+       "      0.031037\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "beta_z[Intercept]   0.001  0.010  -0.015    0.011      0.000    0.001   \n",
-       "beta_z[T_cont]      3.076  0.099   2.900    3.269      0.006    0.004   \n",
-       "beta_z[feature_0]   0.476  0.086   0.323    0.648      0.004    0.002   \n",
-       "beta_z[feature_1]  -0.392  0.047  -0.480   -0.306      0.001    0.001   \n",
-       "beta_z[feature_2]  -0.000  0.018  -0.023    0.019      0.001    0.002   \n",
-       "beta_z[feature_3]  -0.003  0.021  -0.031    0.018      0.001    0.003   \n",
-       "beta_z[feature_4]   0.423  0.049   0.341    0.520      0.003    0.001   \n",
-       "beta_z[feature_5]   0.000  0.009  -0.012    0.008      0.000    0.001   \n",
-       "beta_z[feature_6]   0.003  0.016  -0.012    0.033      0.000    0.001   \n",
-       "beta_z[feature_7]  -0.005  0.019  -0.038    0.015      0.001    0.001   \n",
-       "beta_z[feature_8]  -0.004  0.018  -0.039    0.012      0.001    0.001   \n",
-       "beta_z[feature_9]   0.002  0.014  -0.012    0.013      0.000    0.001   \n",
-       "beta_z[feature_10] -0.001  0.010  -0.011    0.012      0.000    0.001   \n",
-       "beta_z[feature_11]  0.004  0.019  -0.016    0.031      0.001    0.001   \n",
-       "beta_z[feature_12] -0.006  0.023  -0.050    0.010      0.001    0.001   \n",
-       "beta_z[feature_13] -0.001  0.011  -0.012    0.005      0.000    0.001   \n",
-       "\n",
-       "                    ess_bulk  ess_tail  r_hat  \n",
-       "beta_z[Intercept]     1035.0     807.0   1.00  \n",
-       "beta_z[T_cont]         286.0     340.0   1.00  \n",
-       "beta_z[feature_0]      477.0     571.0   1.00  \n",
-       "beta_z[feature_1]     1903.0    1459.0   1.00  \n",
-       "beta_z[feature_2]      464.0     886.0   1.01  \n",
-       "beta_z[feature_3]      357.0     313.0   1.00  \n",
-       "beta_z[feature_4]      380.0     176.0   1.01  \n",
-       "beta_z[feature_5]      629.0    1103.0   1.00  \n",
-       "beta_z[feature_6]      781.0    1101.0   1.00  \n",
-       "beta_z[feature_7]      738.0     472.0   1.01  \n",
-       "beta_z[feature_8]      700.0     973.0   1.00  \n",
-       "beta_z[feature_9]      925.0    1187.0   1.00  \n",
-       "beta_z[feature_10]    1407.0    1059.0   1.00  \n",
-       "beta_z[feature_11]     787.0     727.0   1.01  \n",
-       "beta_z[feature_12]     394.0     684.0   1.01  \n",
-       "beta_z[feature_13]     746.0    1345.0   1.00  "
+       "    shrinkage_factor  lambda_tilde       tau\n",
+       "0           0.024168      1.163944  0.031037\n",
+       "1           1.574053     75.808614  0.031037\n",
+       "2           0.454083     21.869287  0.031037\n",
+       "3           0.416588     20.063451  0.031037\n",
+       "4           0.078152      3.763922  0.031037\n",
+       "5           0.043892      2.113912  0.031037\n",
+       "6           0.388261     18.699190  0.031037\n",
+       "7           0.027243      1.312060  0.031037\n",
+       "8           0.027663      1.332293  0.031037\n",
+       "9           0.026640      1.283037  0.031037\n",
+       "10          0.026496      1.276087  0.031037\n",
+       "11          0.031909      1.536788  0.031037\n",
+       "12          0.024832      1.195942  0.031037\n",
+       "13          0.032776      1.578531  0.031037\n",
+       "14          0.026933      1.297110  0.031037\n",
+       "15          0.024878      1.198152  0.031037"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "az.summary(result_spike_slab.idata, var_names=[\"beta_z\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "838e0726",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(20, 6))\n",
-    "az.plot_posterior(\n",
-    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    "summary = result_horseshoe.model.vs_prior_outcome.get_shrinkage_factors(\n",
+    "    result_horseshoe.idata, \"beta_z\"\n",
     ")\n",
-    "az.plot_posterior(\n",
-    "    result_spike_slab.idata,\n",
-    "    var_names=[\"beta_z\"],\n",
-    "    coords={\"covariates\": [\"T_cont\"]},\n",
-    "    ax=ax,\n",
-    ");"
+    "summary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "127888b7",
+   "execution_count": 134,
+   "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([], dtype=object)"
+       ""
       ]
      },
-     "execution_count": 41,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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SRESkPLp2Ccdp3rf9tlmjUsGxarGv6969O02bNmXChAl89NFHFnWzZs2iffv2jBkzBsgdlfztt9+YMWMG/fv3N7cLCgpi5MiR5tebN2/m2rVrzJkzhwceeACAHj16sHjxYo4ePYqLiwsPPfQQgYGBJCYm8swzzwAQFhZm7uP+++9nxowZPPbYY/z555+4uLgU+71JLo1UioiIyG0xadIkFi5cyG+//WZRvnfvXh599FGLsrZt23Lw4EGys7PNZS1atMjXZ5UqVcwJJUDt2rXx8vKySA5r165NRkaG+fWuXbvo1asXDRo0wMPDg06dOgG5Gzql5DRSKSIiUh5VqpI7amiD+5bUE088QadOnRg3bhz9+vUzl5tMpnxrF00FTLMX9EShG9fJ560LvLEs7yimixcv0rVrV5588knmz59PzZo1OXr0KH/7298qzE7vsqKkUkREpDwyGEo0DW1rkyZNolWrVjRo0MBc9tBDD7Fp0yaLdlu3bqVBgwYlfg51Yfbt28fp06eZOHEi9erVA3LXdIr1NP0tIiIit42/vz/PPfccH3zwgbls+PDhrFu3jrfffpv9+/ezcOFC5s6da7H7u7TUq1cPR0dHPvjgAw4dOsTXX3/N22+/Xer3qYiUVIqIiMhtFRERYTG93bx5cxYvXszSpUt55JFHGD9+PG+99ZbFJp3Scs899/Dpp5+yfPlymjVrxrRp05gyZUqp36ciMpgKWrQgchfav38/Tk5OOqdSRModo9HIH3/8Qf369XF2drZ1OHIXunLlCkeOHMHV1TXfOZXXrl3Dy8vrln1oTaVIBXb4zGWOnc+ibnVHfDwq2zocEREpx5RUilRA5y4bifgulR1HL5jLWtWrRlSwNzUq6z8LIiJSfFpTKVIBRXyXSkrmFd5//hG2v9mR959/hJTMK0SuSrV1aCIiUk4VO6lMTEzEYDAQGRlZBuHc2aKjo83PATUYDPTt29fWIVUoL774osXnHx0dbeuQyqXDZy6z4+gFJnRvTNcmdahVzZmuTeowvrs/249c4PCZy7YOUUREyqE7bqTS29sbb29vW4dxU927dyciIoLevXtblIeFhWEwGNi6datV/ZeHz6As5CXthSWLPXv2JCIigu7du9/ewO4yx87nHu7bysfNory1j7tFvYiISHFo8VQJhIaGWjw3VG6Pnj170rNnT6Kjo4mLi7N1OOVW3eqOAOw4fJauTeqYy7cfzrSoFxERKQ4llSIVjI9HZVrVq8a4uN2YMNHax53thzN5K24PretX0y5wEREpEaumv9evX09gYCAuLi64u7vz/PPPc+zYsXzt0tPTGTFiBL6+vjg5OVGzZk169erFnj17zG1SU1MxGAykpaWRlpZmsXYub/1mVlYWc+bM4amnnqJevXo4OTlRq1YtevbsSXJyconeQ959b/aTmppaor6v7z8sLIxDhw7Ru3dv3NzcqFq1Kk8++SQ///xzsT6DPOvXryckJISaNWvi5OREgwYNGDt2LJcuXbJod/0a2C1btvDUU09Ro0YNi2esmkwmFixYQLt27ahRowZVqlShQYMGDBkyhCNHjlj0d+HCBSIiIvDz86Ny5crUqFGD4OBgNm7cmO+9BwUFYTAYuHLlCv/617+oV68ezs7ONG7cmM8++8yibVhYGOHh4QCEh4dbvHcpfVHB3vh6ODN0cTKtJ61h6OJkfD2ciXzK29ahiYhIOVXikcqtW7cyefJkunbtyrBhw9i5cycxMTFs3LiRHTt2ULt2bQBSUlIICgri+PHjdO7cmdDQUNLT01m+fDmrVq1izZo1BAQEUKNGDSIiIpg5cyaQ+8imPEFBQQBkZmYyfPhwnnjiCbp06YKbmxuHDh0iPj6eb7/9lvXr19OqVativY+8+97o8uXLzJgxg5ycnFI5aDY1NZWAgAAefvhhBg4cSEpKCnFxcbRv357ffvuN2rVrF+kzAPjwww959dVXcXNzIyQkhHvuuYcdO3YwadIk1q1bx7p163B0tJzC3Lx5M2+//Tbt27fn73//uzlZNJlMPPfcc3z55Zfcd999PPfcc7i6upKamsqXX35JcHAw9evXB3I//3bt2vHrr7/yxBNP8NRTT3H+/Hnz+/jqq68IDQ3N996feeYZfvnlF5555hmuXbvG0qVLGTRoEKdOneI///kPkLuk4Ny5c8TFxdG9e3eaNWtm9WcuhatR2YGZob46p1JEREpNiZPKVatW8emnnzJo0CBz2fjx44mIiGDMmDHMmzcPgP79+3Py5ElWrVpFp06dzG3Hjh1Ly5YtGTx4ML/88gs1atQgMjLSvEmjoN3lbm5uHDlyhPvuu8+i/Ndff6VNmzaMGTOG77//vljvI+++1zOZTPTt2xej0ci7776Lp6dnsfosSFJSElOmTOHf//63uWzcuHFMnDiR+fPn88YbbxTpM/jf//7HP/7xD5o1a8YPP/yAu7u7uW7KlCn85z//Yc6cObz++usW133//ffMmzePgQMHWpTPnTuXL7/8ko4dO/L1119TufJficXly5e5fPmvncD/+Mc/+PXXX/nss8/Mo4oAb7/9Nq1ateLvf/87wcHB+ZLwQ4cOsWfPHqpVqwbAm2++ySOPPMJbb71Fnz59uP/++y2SSq1ZvX18PCormRSRO1pqaiqNGjVi+/btNG3alKSkJDp37sypU6eoUaOGrcO7qc8//5xRo0aRnp5+W/to2LAhQ4cOZdiwYSW+b0mUePq7UaNG+RKU0aNHc8899xATE0NWVhbJycls3ryZAQMGWCSUkPuGBw8ezO7duy2mwW/GyckpX0IJ4OfnR/v27Vm/fj3Xrl0r6Vsyi4iIYOnSpYSHhzNq1Cir+wPw8fFh9OjRFmV5CfmOHTuK3M9HH32E0Whk9uzZFgklwL/+9S/z53+j5s2b5/t9Abz//vvY29szd+5ci4QSoHLlyuZ7nD592px8Xp9QAtSuXZvRo0eTkZHBDz/8kO8eb775pjmhBPD09GTkyJEYjUYWL15c5PcuIiLlS3p6Oq+++iq+vr5Uq1aN+vXr07Vr12KdklKvXj3S0tLw8/Mrw0htJzExkc6dO+Pp6UmNGjV4+OGHGTRoEEaj0dahFVuJRyofe+yxfOvdKleuTIsWLfjuu+/Yv3+/+Utz8uTJAkfd9u7da/7T39+/SPfdtWsX77zzDhs3buTkyZP5ksjTp09Tp06dQq6+tZiYGCZMmMATTzzBhx9+WOJ+btS0aVPs7Cxz+Lp16wJw7ty5IveT95l+9913BSZwlSpVMn+u12vdunW+sosXL/K///0PX19fGjRocNP77tixg+zsbK5cuVLg7/LAgQNA7u/yb3/7m0XdE088ka99XtmuXbtuel8RESm/+vbty7Vr1/j000/x8fEhPT2ddevWkZmZWeQ+7O3tS2XG8E70v//9j27duvHaa6/xf//3fzg7O3Pw4EFWrFhBTk6OrcMrthInlbVq1SqwPG8t5fnz581fmoSEBBISEgrt6+LFi0W65+bNm+nQoQMAnTt3pkGDBri4uGAwGIiNjeXnn3/m6tWrxXkbFrZu3crAgQO5//77+e9//5tvXaI1qlevnq8s74Ht2dnZRe4n7zOdNGlSse6f93u5Xl4yW9Dob2H33bRpE5s2bSq0XUG/y4K+K9d/T8S2HM4exP78EbKr18fo5mvrcESkiEwmE5ev3f7Eo3IluyJtojx37hybNm3i+++/p127dgB4eXnl2/vg5OTE7NmzWblyJevXr6d27dpMnjyZXr16Afmnv290+fJl+vbtS2ZmJnFxcbi7u7NgwQKmT59OamoqXl5evPbaawwZMqTQWFetWsWUKVP49ddfsbe3JyAggOnTp/PAAw9YxPDll1/ywQcfsH37dnx9fXnvvfdo06aNuZ/PP/+c8ePHc/r0aTp16sSjjz5608/ohx9+wNPTk8mTJ5vLHnjgAZ566qlCr0lJSeFf//oX27dv5+LFizz44INMmDCBjh07WrT7888/6d+/PytXrsTV1ZXRo0fz2muv3TQea5U4qSxsbv/UqVNAbhLl6uoKwJw5cxg6dGhJb2U2adIkrl69ysaNG3nssccs6rZu3Wqxk7q4jhw5QmhoKI6Ojnz99dfUrFnT2nDLRN5n+scff1hMKd9KQf8ByEt0jx8/XuT7vv7660ybNq3I94Xc70q9evUsyq7/nsjtZ3c5E8OVc9TYGInT738tv7hapxXnHo8k2+1+G0YnIkVx+VoOzSauve333TW2A1Uc7W/ZzsXFBRcXF+Lj4wkICMDJyanQtlFRUUycOJHp06ezePFi+vXrx8MPP8xDDz1003ucP3+eHj164OTkxHfffUfVqlWZN28eEyZMYObMmTRt2pSff/6ZV155hapVq9KvX78C+7l06RL//Oc/8fPz49KlS0RFRfHss8+yY8cOi1nGt956iylTpuDr60tERAT9+/fnf//7Hw4ODmzfvp2///3vTJgwge7du7N69WomTJhw0/g9PT05efIkGzZsKHBWryAXL14kODiYqKgonJyc+OKLL+jZsye7d+82b6wFmDFjBv/+978ZO3Ys33//PaNHj6ZRo0Y8+eSTRbpPSZR4TeWmTZswmUwWZZcvX+ann36icuXKNGzYkICAAAC2bNlS5H7t7e0LHblLSUnB3d09X0J56dIldu7cWcx38Jc///yTkJAQ87rBhx9+uMR9lYabfQZ5n6m1T+2B3P/BP/zwwxw+fNg8fV2YVq1aYTAYivW7zLNhw4ZCy67f5W1vn/sfqeKM3ErhDNcuFfrjufAxai8LwensAXgmGl7fD89E43T2ALWXhdz0WhGRonBwcODTTz/liy++oFatWgQFBTFu3Dh2796dr23Pnj0ZOHAgDRs2JDIykhYtWvDBBx/ctP9Tp07x5JNPcs899xAXF0fVqlUBmDx5MlOnTiU0NBQfHx9CQ0MZNmwYn376aaF99ejRg9DQUBo0aEDTpk356KOP2LNnD7/99ptFuxEjRtClSxcaNmzIW2+9RVpaGgcPHgTgvffeo1OnTowePdq8UebG/SQ36tWrF3369OHJJ5/Ey8uLZ555hg8++IA//vij0GuaNGnC4MGD8ff3p0GDBkRFReHj48PKlSst2rVt29Ycy2uvvUbPnj2ZPXv2TeOxVolHKvft28dnn31msfv73XffJSMjg4EDB+Lo6Ejr1q0JCAggJiaGbt260adPH4s+cnJy2LBhA4GBgeYyd3d39uzZw5UrV/LtIvby8mL//v38+uuv5gW72dnZjBo1ioyMjBK9j5ycHJ5//nl++eUXZs2aRXBwcIn6KU03+wxeffVVPvnkE/7xj3/w/fff5xsBPHfuHIcPH6Z58+ZFutdrr73Ga6+9xquvvkp8fLzFZp0rV65w6dIl3N3d8fT05Nlnn+XLL7/k3XffZdSoUflGP7dt20bjxo2pUqWKRfmkSZMICQkxj6yeOnWKGTNm4ODgwPPPP2/xvoECzzqV4qszv8XNG5hyIGQm+PXIfe3XA0wmWBZ+02tP/P23QutE5PapXMmOXWM72OS+RdWjRw+efvppNm7cyLZt21i9ejXTp0/nww8/pH///uZ2108hQ+4Ayi+//HLTvrt06UKLFi1YvHixeVAiIyODo0eP8vLLL/PKK6+Y2xqNxpvOjKWkpBAVFcW2bds4c+aMeT3jkSNHLDYINW7c2Pz3vHWeGRkZPPjgg+zduzffY4QDAgJYvXp1ofe1t7fnk08+ITIyksTERLZt28bUqVOZPn06GzduLHCPyMWLF5k4cSLffPMNv//+O0ajkcuXL3P06FGLdgV9pnPmzCk0ltJQ4qSyc+fOvPrqqyQkJPDggw+yc+dOVq1aRb169Xj77bfN7WJiYmjfvj19+/Zl5syZtGjRAmdnZ44cOcKWLVvIyMjgypUr5vYdOnTgxx9/JCQkhCeeeAJHR0cef/xxHn/8cf7xj3+wevVqHn/8cZ599lmcnZ1JTEzk+PHjBAUFkZiYWOz3sWzZMr7++mvq1KlDZmZmgZtQhg8ffluPLbjZZ+Dv788HH3zAK6+8QqNGjejSpQsPPPAAf/zxB4cOHSIpKYmwsLAibzJ65ZVXSEpKYunSpTRo0IBu3brh6urKkSNHWLVqFfPmzTOfPfnBBx+wb98+/vWvf7Fw4ULatm1L9erVOXr0KD/99BMHDhzg999/z5dU3n///fj7+9OrVy/zOZXp6elMmjSJ++//a5q1bdu2VK5cmZkzZ/LHH39wzz33APDGG2+Uzgcr+dW/Yb2P12MFtxORO47BYCjSNLStOTs78+STT/Lkk0/y5ptvMmTIECZMmGCRVBbkVus2g4ODiY2N5bfffjNv9s1LBufOnZtv7WZe4lmQnj17UrduXebOnUudOnUwmUw0b96crKwsi3aVKlXKF1/ePW+cvS2O++67jxdeeIEXXniBqKgo/P39+eSTT3jrrbfytX3jjTf4/vvvmTp1Kg888ADOzs4899xz+WItSFk/UKTESWXbtm158803GTt2LLNmzcLR0ZG+ffvyzjvvWGwK8fHxITk5mRkzZhAbG8tnn32Gvb09derUoV27dvTu3dui33HjxnH27FlWrlzJ2rVrycnJISIigscff5y//e1vLFu2jLfffpsvvviCKlWq0KFDB1asWMH48eNL9D7ynkDz+++/ExUVVWCbsLCw25pU3uwzABg8eDDNmjVjxowZrF+/nvj4eKpXr079+vUZMWIEAwYMKPK9DAYDS5YsoXPnznz66ad8/vnnmEwm7rvvPp599llatPhrxMrd3Z3Nmzfz3nvv8eWXX7Jo0SJycnLw9PSkadOmjBs3rsC1qEuXLuWtt94iJiaGjIwMGjRowNtvv20xyp3X/7Jly4iMjGTu3LnmMzKVVJbM7+E/FVpnHok8svmvkUqAtNxNWBk9vsJYQ+sqRaT0Pfjgg8THx1uUbdu2jRdffNH8urBNOdebNGkSLi4uBAcH8/333/PQQw9Ru3Zt7rvvPg4fPsxzzz1XpHjOnDnD3r17ef/9983/zt5sQ2phHnroIbZt22ZRtn379mL34+bmhqenZ6GbmDdt2kT//v3No6J//vknaWlp+doVFEujRo2KHU9xFDupDAoKssjG169ff8tr3NzcmDBhwi0XrELuOr+PP/640PpevXqZd4RdLzo62nxoeHGEhYWV2kHbBcXg7e190//3UlDdrT4DyF3jWNB5lDe68fdVEIPBwKBBg/IleQWpXLkyo0ePznfm5q2ueffdd3n33Xdv2bZLly506dKlyH1L4UyVqhRad7LfJtx+GI5jwusYTKbcEcq0TZi+GUVWndZcu6doR3yJiBTmzJkzPP/88wwYMIDGjRvj4uLCzp07mTFjBiEhIRZt//vf/9KiRQseffRRlixZwo4dO/joo49ueY+pU6eSnZ3NU089xerVq3nwwQcZO3YsI0eOpFq1ajz11FNkZWXx008/cfbsWYsn1eVxc3PDw8ODefPm4enpydGjRxk7dmyx3+9rr71GYGAg06ZNo1u3bvzwww83nfoG+OSTT/j555/p3r07999/P1evXuWLL77gf//7H//3f/9X4DUPPPAAsbGxdO3a1fwY5oKOH9qyZYs5ljVr1rB8+XJiY2OL/b6Kw6pnf1dUec+m7tu3r61DqVBefPFFDAZDvsPXpfhyKrtzttMsstwbwbJwmN4QloWT5d6Is51m2jo8EbkLuLi40KpVK2bPnk3Hjh155JFHiIyMZODAgebHEecZN24cS5cupWXLlnzxxRcsWLDglju/80ybNo1evXoRHBzM/v37GThwIB9++CELFy6kRYsWPPnkkyxcuBAfH58Cr7ezs2PhwoXs3LmTRx55hNGjR1sc8VNUAQEBfPjhh8ydO5fWrVvzww8/3HKmrVWrVly8eJGhQ4fSvHlznnzySbZv385XX31lPobpRu+++y5ubm4EBgbSs2dPOnXqVOA+iuHDh5OcnExAQIB581Lnzp2L/b6Kw2CyZhFABbNr1y6LLN/f3z/f9L38JSgoiKSkJKvWmVzvv//9r8XC7dDQ0GI9I3z//v04OTlZrIkRnVMpUh4YjUb++OMP6tevn28DZ3nn5OTE0qVL821ykdvrypUrHDlyBFdXV/M52nmuXbuGl5fXLfso8ZrKO1lqamqRpsJr1KhR4FB4YZo1a1asJEZKV8+ePenZs6etw7jrGN18lUyKiIjV7tqksrBNN9fz8vIqVlIpxVOS3fgiIiJSPt2VSWVRNqeIiIiI7VnzeGW5s2ijjoiIiIhYTUmliIhIOaAZOClr1n7HlFSKiIjc4ezscv+5znsohEhpy/tu5X3XSuKuXFMpIiJyN7Gzs8PR0ZGMjAwg96ESIqXl8uXLZGRk4OjoqKRSpKiMRqOtQxARKREnJydycnI4depUmT/DWSoWk8lEpUqVcHJy4tq1a/nqi/pvp5JKqTAcHR3JysoiOzvb1qGIiJSIvb09dnZ2mEwmrbGUUmEwGMw/WVlZ1vWlJ+pIRZGdnV3g81FFRETk5oryNDollSIiIiJiNe3+FhERERGrKakUEREREaspqRQRERERqympFBERERGrKakUEREREaspqRQRERERq+nwcxERESl3Dpy6QOqZS3h7VKFB7Wq2DkdQUikiIiJ3uDN/XjX//eylLN5csYdthzPNZQE+7kzq4Y9bFUdzmYeL022NUXT4uYiIiNjApayiPU8a4OG3Vpn/bmeAas6VeLtHY1r5uLHj8FnGrNjNhSvXyLkuo/nf+KeKFU8VR42zWUtJ5R0kOjqa8PBw8+s+ffqwZMkSG0Z05zh48CANGjQwv/by8iI1NdV2AYmIiFW830go8bXvP/8IXZvUMb9e+csJhi5Otiqe1CldrbpetFHnjtS9e3ciIiLo3bs3kZGRFg97v9VPZGSkrcMvMW9vb7y9vQusc3d3JyIigoiICKpXr357AxMRkTtKKx83i9etfdxtFIlcT2O9d6DQ0FDCwsIAqFmzZr76xMREkpKS6N69O82aNbOoCwoKKvsAbcDd3d2cMEdHR9s0FhERsV5xpqevn/4G2HH4rMVI5fbr1leWpH8pHUoq73BBQUH5EsXIyEiSkpIskk8REZHypDhrGH8a+6T5768u2sm4uD2YMNHax53thzN5K+5XAnzc+eCFR0rUv5QOTX8L6enpjBo1ikaNGuHs7Iy7uztt2rRh+vTp+dquXLmS9u3bU716dSpXrkyzZs2YOXMm2dnZFu1SU1MxGAyEhYVx6NAhevfujZubG1WrVuXJJ5/k559/ztc2LS2NtLS0u2Y6X0RESoeHi5P5Z+6LLfC715Whi5NpPWkNQxcn43evK3NfbGHRTm4/pfEV3IEDB2jfvj3Hjx/n8ccfJzQ0lIsXL7Jnzx4mTZrE66+/bm47a9Yshg8fjru7O88//zxVq1bl66+/ZsSIEWzYsIFly5ZhMBgs+k9NTSUgIICHH36YgQMHkpKSQlxcHO3bt+e3336jdu3a1KhRg4iICGbOnAnA8OHDzdffrdP5IiJSMu5VHVk4KEDnVN6JTHLHmD9/vgkwzZ8//6btIiIiitSuKFq3bm0CTB9//HG+uqNHj5r/npKSYnJwcDDVqlXLdOTIEXP51atXTYGBgSbAtHDhQnP54cOHTYAJME2ZMsWi37Fjx5oA0+TJky3Kvby8TF5eXreMuajtRERE5PbR9HcFtmPHDrZv3067du0YPHhwvvq6deua/75o0SKMRiOvv/469erVM5c7OjoyZcoUoOANND4+PowePdqibNCgQeb7i4iIyN1BSWUFtn37dgA6d+58y7bJybnnfxU0Hd2mTRsqV67Mrl278tU1bdoUOzvLr1lesnru3LniBSwiIiJ3LCWVFVheUnfffffdsu0ff/wBQO3atQusr1WrFufPn89XXtCZkg4OuUt5b9zcIyIiUurS98Leb3L/lDKljToVWI0aNQA4fvz4Ldu6uroCcOrUKby8vPLVp6enm9uIiIgU2cXTZdPvpUxYORzSNv1V5vUY/G0mVCmlw9Kr5j9LuiJTUlmBtW7dGoDVq1fz5ptv3rRt8+bNWbFiBYmJiebr8mzfvp3Lly/Ttm1bq+Kxt7cnKyvLqj5ERCqUrIu2jsB67z5QNv0a7MDJFZ6JhvqPwpHN8PVw+CAATDmlc48xJ0qnn9LmWNUmt1VSWYG1atWK1q1bs379ej755JN8m3WOHz9unhp//vnnGT9+PDNmzODFF1/k3nvvBeDatWu88cYbAFYfxO7u7s6ePXu4cuUKzs7OVvUlIlIhvH2vrSO4c5lyIGQm+PXIfe3XA0wmWBZeeve4Uz//yPzL0W4HJZUV3BdffEFQUBB///vfWbhwIW3btuXKlSv8+uuvJCcnc+bMGQAeeOABpk6dyuuvv06TJk149tlnqVq1KitXrmTv3r10796dF1980apYOnTowI8//khISAhPPPEEjo6OPP744zz++OOl8VZFRKSiqf+o5Wuvx2wTRwWhpLKCa9CgATt37mTy5Ml8/fXXzJw5ExcXFxo0aMDYsWMt2o4cORJfX19mzJjBF198QVZWFg0bNmT69OkMGzYs38HnxTVu3DjOnj3LypUrWbt2LTk5OURERCipFBEpzJ06/VocZTnad2TzXyOVYLm+sjTcDZ9/KTKYTCaTrYOQXNHR0YSHhzN//nw90/smvL29gdyn9YiISDlXVht1lvaHjH3Q5d3cEcq0TfDNaLjnQXh2QencQxt1LCipvIPkJZV5+vTpw5IlS2wY0Z3j4MGDNGjQwPzay8tLSaWIiBTu4hlYPggOrfur7P720GseVPWwXVx3MU1/30GaNWtGRESE+bW/v78No7mzuLu7W3w2ecchiYiIFKiqB/SPzT2fMvMQuN8PtR60dVR3NY1U3mViY2MLfLLNjYKCggp8Oo6IiIhISWik8i4TGxvLggVFWyuipFJERERKi0YqRURERMRqeva3iIiIiFhNSaWIiIiIWE1JpYiIiIhYTUmliIiIiFhNu7+lwsjOziYnJ8fWYYiIiJQ7lSpVumUbJZVSYRw9epSsrCxbhyEiIlLuNGzY8JZtlFRKhZGVlYW9vT0ODvrai4iIFJXRaCxSO/3rKhWKg4NDkYbwRUREpHi0UUdERERErKakUkRERESspqRSRERERKympFJERERErKakUkRERESspqRSRERERKympFJERERErKZzKkWkXDp85jLHzmdRt7ojPh6VbR2OiEiFp6RSRO54Zy9dM//9/BUj7647RvLxP81lze9zYXT7ulR3dsCtig63FxGxBSWVNxEdHU14eLj5dZ8+fViyZIn59bZt2/jPf/7Dzz//TGZmJoGBgSQmJtog0ruf0WjM9yQck8lko2ikNF2+ln3LNl0/3WP+u50BqjlX4v3nH6GVjxs7Dp9lzIrdvLhoLzkmWPNKk2Ldv3Il+2LHLCIi+SmpLILu3bvTrFkz/P39zWXnz58nJCSErKws+vXrh4eHB97e3rclHoPBcNcksIsXL2bmzJn8+uuvODo60rZtW8aPH0/Lli0t2tnZ2REREQHkJvtpaWm2CFfKQMe5vxSrfY4J3u7RmK5N6gDQtUkdTJgYuji5RP1tHta8WO1FRKRgSiqLIDQ0lLCwMIuyHTt2kJGRweTJk3njjTdsE1g59/bbb/Pmm29Sv359hgwZwp9//smSJUt47LHHWLVqFUFBQea2dnZ2REZGApCYmKiksoJr5eNm8bq1j7uNIhERkTxKKkvoxIkTAHh6eto4kvLpwIEDRERE0LBhQ7Zv30716tUBGDZsGK1bt+all15i7969ODjoK3q3K8p09Y2jjzsOnzWPVAJsP5xZrP5ERKT06UihEjAYDAwYMACA8PBwDAYDBoPBYjo6PT2dESNG4Ovri5OTEzVr1qRXr17s2bMnX3/r1q1j4MCBNGrUCBcXF1xcXGjZsiUff/yxRbvExEQMBgMASUlJ5vsaDAaio6MBiIyMzBdLnujoaIu2AKmpqRgMBsLCwti7dy89e/akZs2aGAwGUlNTze3i4uLo2LEjbm5uODs74+/vz7Rp08jOvvV6uILMnz8fo9HIm2++aU4oAfz8/Ojfvz8pKSmsXbu2RH1L+VK5kv0tfxJe8jf/NL/PhXFxu1n5ywnSL1xh5S8neCtuD83vcyHhJf8i9Xf9j4iIlA4NA5VAREQEu3btIi4uzrzeEjCvqUxJSSEoKIjjx4/TuXNnQkNDSU9PZ/ny5axatYo1a9YQEBBg7m/q1KkcPHiQNm3a0KNHD86dO8d3333Hyy+/zL59+5g+fbq5/4iICKKiovDy8rKYks+LoaTy7u/n58eAAQPIzMzE0dERgDFjxjB58mTq1q1Lr169cHV1Zf369YwePZpt27bx1VdfFft+eUlv586d89U99dRTfPjhhyQlJRVYLxXP9Tu6J3XxIXJVqnkNJUDr+tWIfMqbGpX1nzQREVvRf4FLIDIykujoaOLi4gpcb9m/f39OnjzJqlWr6NSpk7l87NixtGzZksGDB/PLL39N582dOxcfHx+LPoxGI126dGHWrFn885//pH79+nh7exMZGUlUVJT576Vl06ZNjBs3jvHjx1uUf//990yePJmnn36aZcuWUaVKFSB35/Wrr77Khx9+yPLly+nVq1ex7nfgwAFcXFwKXD7QoEEDcxuRG9Wo7MDMUF+dUykicofR9HcpS05OZvPmzQwYMMAioQRo2LAhgwcPZvfu3RbT4DcmlAAODg4MGTKE7Oxs1q1bV+Zxe3p6Mnbs2Hzl7733HgAfffSROaGE3CUAU6ZMwWAwEBMTU+z7nT9/3mLa+3qurq7mNiKF8fGozBP3V1dCKSJyh9BIZSnbunUrACdPnixwJHHv3r3mP/OOKLpw4QLTpk0jNjaWlJQULl68aHFN3qagstS0aVPzdPf1tm7dStWqVZk3b16B11WuXNn8nkRERKTiUlJZyjIzc3ehJiQkkJCQUGi7vMQxKyuLoKAgdu7cSfPmzc1nXjo4OJCamsqCBQu4evVqmcddu3btAsszMzMxGo1ERUUVeu2NSXBRVK9evdCRyD/++MPcRkRERMoHJZWlLG/qds6cOQwdOvSW7ePi4ti5cycvvfQSn3zyiUXdkiVLWLBgQbHub2eXu6LBaDTmq7vZdHLervIbubq6YjAYOH36dLHiuJUGDRqwZcsWTp48mW9dZd5ayry1lVJxOZw9iP35I2RXr4/RzdfW4YiIyE1oTWUpy9vVvWXLliK1T0lJAaBbt2756jZs2FDgNXZ2doUe5ePmlnso9PHjx/PVJScn5yu7lYCAAM6cOVPqm2YCAwMBWL16db66VatWWbSRu4fd5cwi/difPYTH1/2p9VUIHqtfy/3z6/7Ynz1U5D6u/xERkbKnkcpS1rp1awICAoiJiaFbt2706dPHoj4nJ4cNGzaYEyYvLy8ANm7cSEhIiLldUlJSvpHLPO7u7hw7dqzAurzHG37++ef069fPPHK5ZcsWFi1aVOz3M2zYML799lsGDhxIbGwsHh4eFvUnT57k7NmzPPTQQ8XqNzw8nGnTpjFp0iS6d+9unur+9ddf+fzzz3nggQfo0KFDseOVkjFcu3Rb7uO58LGiNTTYgZMrPBMN9R+FI5tx+no4tZeFgCmn2Pf9PfynYl9TmkyVqty6kYhIOaeksgzExMTQvn17+vbty8yZM2nRogXOzs4cOXKELVu2kJGRwZUrVwAICQnB29ubd955hz179uDv78++fftYuXIloaGhLF++PF//HTp0YOnSpfTu3ZvmzZtjb29P165dady4MW3atKFt27asXbuWtm3b0q5dO9LS0oiPjyckJIQVK1YU670EBwczbtw4JkyYgK+vL8HBwXh5eXHmzBkOHjzIhg0bmDhxYrGTyoYNGxIZGcnYsWNp0qQJvXv35uLFi8TExHDt2jU++eQTPU3nNqozv4WtQ7BkyoGQmeDXI/e1Xw8wmWBZeIm6s/X7O/H332x6fxGR20H/apcBHx8fkpOTmTFjBrGxsXz22WfY29tTp04d2rVrR+/evc1tXVxcWLt2LaNHj2b9+vUkJibi5+fHokWLqF27doFJ5axZswBYu3YtK1asICcnB09PTxo3bozBYCA+Pp6RI0eSkJDA7t27adq0KfHx8Zw4caLYSSXA+PHjadeuHbNnz2bNmjWcO3cODw8PfHx8iIyM5IUXXijR5/Tmm2/i7e3NzJkzmTt3Lo6Ojjz66KOMHz+eVq1alahPuYvUf9TytVcRRzlFRMQmDCaTyWTrIO5U0dHRhIeHM3/+/HwHnIvtBAUFkZSURHG/uvv378fJyYlKlSrdunEFc7umv4s1YvhM9F8jlQB7/lvikUpNf4uIlNy1a9fMy/VuRiOVRRAeHk54eDh9+vRhyZIltg6nQjIajUoGy9DtSnpO9ttUpHZuPwzHMeF1DCZT7ghl2iZM34wiq05rzj75f8W+r5I6EZGyp6TyJpo1a0ZERIT5dd5h5XL72dnZWfwupHzKqexepHZnO83Cbe0onK4bmcyq+yhnO0wjx9mtrMITEREraPpbSkVqairR0dG3bFejRg2GDx9e5vEURNPf5Y/OqRQRsb2iTn8rqZRSkZiYSPv27W/ZzsvLi9TU1LIPqABKKkVERIpPayrltgoKCir2xhkRERG5e+iJOiIiIiJiNSWVIiIiImI1JZUiIiIiYjUllSIiIiJiNSWVIiIiImI17f6WCsVoNNo6BBERkXKlqP92KqmUCsPR0ZGsrCyys7NtHYqIiMhdR4efS4WRnZ1NTk6OrcMQEREpd4ry4BAllSIiIiJiNW3UERERERGrKakUEREREaspqRQRERERqympFBERERGrKakUEREREaspqRQRERERq+nwc5EyduDUBVLPXMLbowoNalezdTgiIiJlQkmlSBk48+dVzl7K4s0Ve9h2ONNcHuDjztwXW+Be1dGG0YmIiJQ+HX4uYqVLWfmfifrwW6uwM0A150q83aMxrXzc2HH4LGNW7MbvXlc+HdDSon0VR/3/OxERKd+UVN5EdHQ04eHh5td9+vRhyZIl5tfbtm3jP//5Dz///DOZmZkEBgaSmJhog0jvfkajMd8jou6Ur673GwmF1r3//CN0bVLH/HrlLycYujg5X7vUKV3LJDYREZHbRcMjRdC9e3eaNWuGv7+/uez8+fOEhISQlZVFv3798PDwwNvb+7bEYzAYyn0Ce+nSJebOnctPP/3Ezp072b9/PyaTicOHDxf4OdrZ2REREQHkJvtpaWm3OeKSaeXjZvG6tY+7jSIREREpW0oqiyA0NJSwsDCLsh07dpCRkcHkyZN54403bBNYOZaens6oUaMA8PLyws3NjczMzELb29nZERkZCUBiYuIdlVT+b/xT+coefmsVADsOn7UYqdz+/9dXxg99DN9aLrcnQBERkdtARwqV0IkTJwDw9PS0cSTlU82aNVm9ejVnzpwhNTWVVq1a2TqkEqvi6JDv56exTxLg4864uD2s/OUE6ReusPKXE7wV9ytPNKhJk7o1LNqLiIiUd0oqS8BgMDBgwAAAwsPDMRgMGAwGi+no9PR0RowYga+vL05OTtSsWZNevXqxZ8+efP2tW7eOgQMH0qhRI1xcXHBxcaFly5Z8/PHHFu0SExMxGAwAJCUlme9rMBiIjo4GIDIyMl8seaKjoy3aAqSmpmIwGAgLC2Pv3r307NmTmjVrYjAYSE1NNbeLi4ujY8eOuLm54ezsjL+/P9OmTSM7O7tEn6GLiwudOnXC3f3unA72cHFi7ost8LvXlaGLk2k9aQ1DFyfjd68rs/o2t3V4IiIipU5DJCUQERHBrl27iIuLM6+3BMxrAVNSUggKCuL48eN07tyZ0NBQ0tPTWb58OatWrWLNmjUEBASY+5s6dSoHDx6kTZs29OjRg3PnzvHdd9/x8ssvs2/fPqZPn27uPyIigqioKLy8vCym5PNiKKm8+/v5+TFgwAAyMzNxdMw99mbMmDFMnjyZunXr0qtXL1xdXVm/fj2jR49m27ZtfPXVV1bd+27lXtWRhYMCdE6liIhUDCYp1Pz5802Aaf78+cWqe/TRR00ODg6m1atXW5Tv27fPVK1aNVPjxo0tyg8dOpSvj2vXrpk6depksre3N6WlpVnUAabAwMACY46IiDABpnXr1hUp5sOHD5sAE2AaN25cvmtWr15tAkxPP/206eLFi+bynJwc05AhQ0yAadmyZQXGUhxPPfWUCTAdPnz4lm0DAwNN+uqKiIjcWTT9XcqSk5PZvHkzAwYMoFOnThZ1DRs2ZPDgwezevdtiGtzHxydfPw4ODgwZMoTs7GzWrVtX5nF7enoyduzYfOXvvfceAB999BFVqlQxlxsMBqZMmYLBYCAmJqbM4xMREZE7m6a/S9nWrVsBOHnypHm38vX27t1r/jPviKILFy4wbdo0YmNjSUlJ4eLFixbX5G0KKktNmzY1T3dfb+vWrVStWpV58+YVeF3lypXN70lEREQqLiWVpSzvWJyEhAQSEgo/FDsvcczKyiIoKIidO3fSvHlz85mXDg4OpKamsmDBAq5evVrmcdeuXbvA8szMTIxGI1FRUYVee2MSLDdI3wuZh8D9fqj1oK2jERERKRNKKkuZq6srAHPmzGHo0KG3bB8XF8fOnTt56aWX+OSTTyzqlixZwoIFC4p1fzu73BUNRmP+RweeP3++0OvydpXfyNXVFYPBwOnTp4sVhwAZ+2HlcEjb9FeZ12Pwt5lwT0NbRSUiIlImtKaylOXt6t6yZUuR2qekpADQrVu3fHUbNmwo8Bo7O7tCj/Jxc8t9gsvx48fz1SUn53884K0EBARw5swZDhw4UOxrK4Ssi4X/fBAAp36FZ6Lh9f25f576Nbf8ZteJiIiUQxqpLGWtW7cmICCAmJgYunXrRp8+fSzqc3Jy2LBhA4GBgUDu02QANm7cSEhIiLldUlJSvpHLPO7u7hw7dqzAupYtWwLw+eef069fP/PI5ZYtW1i0aFGx38+wYcP49ttvGThwILGxsXh4eFjUnzx5krNnz/LQQw8Vu++7wtv33rw+ZCb49cj9u18PMJlgWfjNr4ssfERZRETkTqWksgzExMTQvn17+vbty8yZM2nRogXOzs4cOXKELVu2kJGRwZUrVwAICQnB29ubd955hz179uDv78++fftYuXIloaGhLF++PF//HTp0YOnSpfTu3ZvmzZtjb29P165dady4MW3atKFt27asXbuWtm3b0q5dO9LS0oiPjyckJIQVK1YU670EBwczbtw4JkyYgK+vL8HBwXh5eXHmzBkOHjzIhg0bmDhxYomSylGjRpmn1Xfv3m0uc3HJfXzhG2+8wYMPlvM1iPUftXzt9Zht4hARESljSirLgI+PD8nJycyYMYPY2Fg+++wz7O3tqVOnDu3ataN3797mti4uLqxdu5bRo0ezfv16EhMT8fPzY9GiRdSuXbvApHLWrFkArF27lhUrVpCTk4OnpyeNGzfGYDAQHx/PyJEjSUhIYPfu3TRt2pT4+HhOnDhR7KQSYPz48bRr147Zs2ezZs0azp07h4eHBz4+PkRGRvLCCy+U6HNatmxZvmd4X/9+w8LC7vykckwhO/Mz9sMnQXBk818jlfDX+srBiVpXKSIidxWDyWQy2TqIO1V0dDTh4eHMnz/f4uk1YltBQUEkJSVxx39153eBjH3Q5d3cEcq0TfDNaLjnQQgv/GQAERGR8khJ5U3kJZV5+vTpw5IlS2wYUcVlNBqpVKmSRdkd/9W9eAaWD4JD1x1ef3976DUPqnoUfp2IiEg5pOnvm2jWrBkRERHm13mHlcvtZ2dnZ/G7KBeqekD/WJ1TKSIiFYJGKqVUpKamEh0dfct2NWrUYPjw4WUej4iIiNxeSiqlVCQmJtK+fftbtvPy8iI1NbXsAxIREZHbSkmliIiIiFhNT9QREREREaspqRQRERERqympFBERERGrKakUEREREavpnEqpMLKzs8nJybF1GCIiIuXOjQ8gKYiSSqkwjh49SlZWlq3DEBERKXcaNmx4yzZKKqXCyMrKwt7eHgcHfe1FRESKymg0Fqmd/nWVCsXBwaFIQ/giIiJSPNqoIyIiIiJWU1IpIiIiIlZTUikiIiIiVlNSKSIiIiJWU1IpIiIiIlZTUikiIiIiVtORQiIV2OEzlzl2Pou61R3x8ahs63BERKQcU1IpUgGdu2wk4rtUdhy9YC5rVa8aUcHe1Kis/yyIiEjxafpbpAIa9+1hUjKv8P7zj7D9zY68//wjpGReIXJVqq1DExGRckpJ5U1ER0djMBjMP3379rWo37ZtGx06dMDDwwODwUBQUJBtAq0AjEajxe/CYDDYOqRy6/CZy/x07E8mdG9M1yZ1qFXNma5N6jC+uz/bj1zg8JnLtg5RRETKIc1zFUH37t1p1qwZ/v7+5rLz588TEhJCVlYW/fr1w8PDA29v79sSj8FgIDAwkMTExNtyv7Kwa9culi9fzvfff8+hQ4c4f/489913H8HBwbz55pvcd999Fu3t7OyIiIgAcpP9tLQ0W4R9Vzh2PguAVj5uFuWtfdzN9VpfKSIixaWksghCQ0MJCwuzKNuxYwcZGRlMnjyZN954wzaBlWNDhgxh+/bttGrVir59++Lk5MS2bduYO3cuX331FRs2bODBBx80t7ezsyMyMhKAxMREJZVWqFvdEYAdh8/StUkdc/n2w5kW9SIiIsWhpLKETpw4AYCnp6eNIymfXnzxRRYtWsQDDzxgUT516lTeeOMNXn/9dRISEmwU3d3Nx6MyrepVY1zcbkyYaO3jzvbDmbwVt4fW9atplFJEREpEaypLwGAwMGDAAADCw8PNa/yun45OT09nxIgR+Pr64uTkRM2aNenVqxd79uzJ19+6desYOHAgjRo1wsXFBRcXF1q2bMnHH39s0S4xMdG8ljApKclifWF0dDQAkZGR+WLJk7dGNK8tQGpqKgaDgbCwMPbu3UvPnj2pWbMmBoOB1NRUc7u4uDg6duyIm5sbzs7O+Pv7M23aNLKzs0v0GQ4dOjRfQgkwatQoqlSpQlJSUon6laKJCvbG18OZoYuTaT1pDUMXJ+Pr4UzkU962Dk1ERMopjVSWQEREBLt27SIuLs683hIwr6lMSUkhKCiI48eP07lzZ0JDQ0lPT2f58uWsWrWKNWvWEBAQYO5v6tSpHDx4kDZt2tCjRw/OnTvHd999x8svv8y+ffuYPn26uf+IiAiioqLw8vKymJLPi6Gk8u7v5+fHgAEDyMzMxNExdxp0zJgxTJ48mbp169KrVy9cXV1Zv349o0ePZtu2bXz11VdW3ft6BoMBe3t77Oz0/3fKUo3KDswM9dU5lSIiUmqUVJZAZGQk0dHRxMXFFbjesn///pw8eZJVq1bRqVMnc/nYsWNp2bIlgwcP5pdffjGXz507Fx8fH4s+jEYjXbp0YdasWfzzn/+kfv36eHt7ExkZSVRUlPnvpWXTpk2MGzeO8ePHW5R///33TJ48maeffpply5ZRpUoVAEwmE6+++ioffvghy5cvp1evXqUSx7Jly7hw4QLPPPNMqfQnN+fjUVnJpIiIlAoNB5Wy5ORkNm/ezIABAywSSoCGDRsyePBgdu/ebTENfmNCCeDg4MCQIUPIzs5m3bp1ZR63p6cnY8eOzVf+3nvvAfDRRx+ZE0rIHVGcMmUKBoOBmJiYUonh6NGjDBs2jMqVKzNhwoRS6VNERERuD41UlrKtW7cCcPLkyQJHEvfu3Wv+M++IogsXLjBt2jRiY2NJSUnh4sWLFtfkbQoqS02bNjVPd19v69atVK1alXnz5hV4XeXKlc3vyRqZmZl06dKF9PR0Pv/8cxo1amR1nyIiInL7KKksZZmZuceyJCQk3HT3cl7imJWVRVBQEDt37qR58+bmMy8dHBxITU1lwYIFXL16tczjrl27doHlmZmZGI1GoqKiCr32xiS4uM6ePcuTTz7Jr7/+yty5c3nxxRet6k9KzuHsQezPHyG7en2Mbr62DkdERMoRJZWlzNXVFYA5c+YwdOjQW7aPi4tj586dvPTSS3zyyScWdUuWLGHBggXFun/eBhej0Ziv7vz584VeV9gTalxdXTEYDJw+fbpYcRRVZmYmTz75JMnJybz//vu8/PLLZXKfis7ucuZN6w1XzlFjYyROv+8wl12t04pzj0dicq5R4DU5ld1LM0QRESnnlFSWsrxd3Vu2bClSUpmSkgJAt27d8tVt2LChwGvs7OwKPcrHzS33KSnHjx/PV5ecnHzLeG4UEBDAt99+y4EDB2jQoEGxr7+Z6xPKOXPm8Oqrr5Zq/+WZ4dqlUu3Pc+Fjt7ihHTi5wjPRUP9ROLIZp6+HU3tZCJhyCrzk9/CfSi0+U6Uqt24kIiJ3NCWVpax169YEBAQQExNDt27d6NOnj0V9Tk4OGzZsIDAwEAAvLy8ANm7cSEhIiLldUlJSvpHLPO7u7hw7dqzAupYtWwLw+eef069fP/PI5ZYtW1i0aFGx38+wYcP49ttvGThwILGxsXh4eFjUnzx5krNnz/LQQw8Vq9/MzEw6duzIrl27mDVrVpES8IqkzvwWt/eGphwImQl+PXJf+/UAkwmWhRd6SWnGeOLvv5VaXyIiYhtKKstATEwM7du3p2/fvsycOZMWLVrg7OzMkSNH2LJlCxkZGVy5cgWAkJAQvL29eeedd9izZw/+/v7s27ePlStXEhoayvLly/P136FDB5YuXUrv3r1p3rw59vb2dO3alcaNG9OmTRvatm3L2rVradu2Le3atSMtLY34+HhCQkJYsWJFsd5LcHAw48aNY8KECfj6+hIcHIyXlxdnzpzh4MGDbNiwgYkTJxY7qezZsye7du3iwQcfJDMzs8BNTcOHD6dGjRrF6lesUP9Ry9detxjdFBERuY6SyjLg4+NDcnIyM2bMIDY2ls8++wx7e3vq1KlDu3bt6N27t7mti4sLa9euZfTo0axfv57ExET8/PxYtGgRtWvXLjCpnDVrFgBr165lxYoV5OTk4OnpSePGjTEYDMTHxzNy5EgSEhLYvXs3TZs2JT4+nhMnThQ7qQQYP3487dq1Y/bs2axZs4Zz587h4eGBj48PkZGRvPDCC8XuM+9pPXv37i10E1BYWFiFTSpLc2oZijiqeGTzXyOVAGmbbtq8tGMUEZHyzWAymUy2DuJOFR0dTXh4OPPnz893wLnYTlBQEElJSRT3q7t//36cnJyoVKlSGUV257rVRh23H4bjeP4Qhi7Tckco0zZh+mYUWdUf4OyT/1fgNdqoIyJSMVy7ds28XO9mlFTeRF5SmadPnz4sWbLEhhFVXEajMV8yqKSy9NhdOYvb2lE4HdtsLrta91HOdphGjrObDSMTERFbK2pSqenvm2jWrBkRERHm13mHlcvtZ2dnZ/G7kNKV4+zGmS7zdE6liIiUmEYqpVSkpqYSHR19y3Y1atRg+PDhZR5PQTRSKSIiUnya/pbbKjExkfbt29+ynZeXl3mTzu2mpFJERKT4NP0tt1VQUFCx1ziKiIjI3cPO1gGIiIiISPmnpFJERERErKakUkRERESspqRSRERERKympFJERERErKbd31KhGI1GW4cgIiJSrhT1304llVJhODo6kpWVRXZ2tq1DERERuevo8HOpMLKzs8nJybF1GCIiIuVOUR4coqRSRERERKymjToiIiIiYjUllSIiIiJiNSWVIiIiImI1JZUiIiIiYjUllSIiIiJiNSWVIiIiImI1HX4uUoEdOHWB1DOX8PaoQoPa1WwdjoiIlGNKKkUqoMyLWQyLSWbjwdPmssd9azL7uea4V3W0YWQiIlJe6fBzkQrmUpaRQdE/sv/UBcZ396eVjxs7Dp/lrbg9PHyvKwsHBdg6RBERKYe0pvImoqOjMRgM5p++ffta1G/bto0OHTrg4eGBwWAgKCjINoFWAEaj0eJ3YTAYbB1SufXwW6vYcugM47v707VJHWpVc6ZrkzpEdfdjw4HTHDh1wdYhiohIOaTp7yLo3r07zZo1w9/f31x2/vx5QkJCyMrKol+/fnh4eODt7X1b4jEYDAQGBpKYmHhb7lcWfv75Z2bOnMlPP/3E8ePHuXjxIvfddx8tWrTgX//6Fy1btrRob2dnR0REBJCb7Kelpdki7LtKKx83i9etfdwBSD1zSesrRUSk2JRUFkFoaChhYWEWZTt27CAjI4PJkyfzxhtv2CawcmzHjh188803tG3blsDAQKpWrcqhQ4f4+uuvWbZsGZ9//jkvvviiub2dnR2RkZEAJCYmKqm0QvzQx+j23iZ2HD5L1yZ1zOXbD2cC4O1RxVahiYhIOaaksoROnDgBgKenp40jKZ9efPFFXnrppXzlv/76Ky1btuT111/nhRde0DR3GWhStwaP+9bkrbg9mDDR2sed7YcziYj7lSca1NQopYiIlIjWVJaAwWBgwIABAISHh5vX+F0/HZ2ens6IESPw9fXFycmJmjVr0qtXL/bs2ZOvv3Xr1jFw4EAaNWqEi4sLLi4utGzZko8//tiiXWJiojnJSkpKslhfGB0dDUBkZGS+WPLkrRHNawuQmpqKwWAgLCyMvXv30rNnT2rWrInBYCA1NdXcLi4ujo4dO+Lm5oazszP+/v5MmzaN7OzsEn2Gzs7OBZb7+fnx0EMPkZ6ezh9//FGivuXWZj/XnIfvdWXo4mRaT1rD0MXJPHyvK7P6Nrd1aCIiUk5ppLIEIiIi2LVrF3Fxceb1loB5TWVKSgpBQUEcP36czp07ExoaSnp6OsuXL2fVqlWsWbOGgIC/dthOnTqVgwcP0qZNG3r06MG5c+f47rvvePnll9m3bx/Tp0839x8REUFUVBReXl4WU/J5MZRU3v39/PwYMGAAmZmZODrmHi0zZswYJk+eTN26denVqxeurq6sX7+e0aNHs23bNr766iur7n29lJQU9u3bR7169ahevXqp9SuW3Ks6snBQgM6pFBGR0mOSQs2fP98EmObPn1+sukcffdTk4OBgWr16tUX5vn37TNWqVTM1btzYovzQoUP5+rh27ZqpU6dOJnt7e1NaWppFHWAKDAwsMOaIiAgTYFq3bl2RYj58+LAJMAGmcePG5btm9erVJsD09NNPmy5evGguz8nJMQ0ZMsQEmJYtW1ZgLEWRnJxsioiIMI0ZM8b0wgsvmKpVq2aqUqWKKSEhodBrAgMDTfrqioiI3Fk0/V3KkpOT2bx5MwMGDKBTp04WdQ0bNmTw4MHs3r3bYhrcx8cnXz8ODg4MGTKE7Oxs1q1bV+Zxe3p6Mnbs2Hzl7733HgAfffQRVar8tYHDYDAwZcoUDAYDMTExJb7vrl27iIqK4u2332bRokVUqVKFFStW0KVLlxL3KSIiIrefpr9L2datWwE4efKkebfy9fbu3Wv+M++IogsXLjBt2jRiY2NJSUnh4sWLFtfkbQoqS02bNjVPd19v69atVK1alXnz5hV4XeXKlc3vqSTCwsIICwvjypUrHDhwgOnTp/P0008zdepURo0aVeJ+RURE5PZSUlnKMjNzj2VJSEggISGh0HZ5iWNWVhZBQUHs3LmT5s2bm8+8dHBwIDU1lQULFnD16tUyj7t27doFlmdmZmI0GomKiir02huT4JJwdnamcePGREdHk5GRwb///W+Cg4MtzgaVEkrfC5mHwP1+qPWgraMREZG7lJLKUubq6grAnDlzGDp06C3bx8XFsXPnTl566SU++eQTi7olS5awYMGCYt3fzi53RYPRaMxXd/78+UKvK+zoHldXVwwGA6dPny6wvix07tyZb775hg0bNiipLKqLBfx+LmXCyuGQtumvMq/H4G8zoYp7wf1UrVkW0YmISAWgpLKU5e3q3rJlS5GSypSUFAC6deuWr27Dhg0FXmNnZ1foUT5ubrlPSTl+/Hi+uuTk5FvGc6OAgAC+/fZbDhw4QIMGDYp9fUnkTfc7OFSwr2eWFSO+7z6Qv8xgB06u8Ew01H8UjmyGr4fDBwFgyim4nzGlvNTCsWrp9iciInesCvavdtlr3bo1AQEBxMTE0K1bN/r06WNRn5OTw4YNGwgMDATAy8sLgI0bNxISEmJul5SUlG/kMo+7uzvHjh0rsC7v8Yaff/45/fr1M49cbtmyhUWLFhX7/QwbNoxvv/2WgQMHEhsbi4eHh0X9yZMnOXv2LA899FCx+t20aRMBAQH5Esddu3bx4Ycf4uDgkG+j013v7XtLtz9TDoTMBL8eua/9eoDJBMvCb18MkYWPjouIyN1FSWUZiImJoX379vTt25eZM2fSokULnJ2dOXLkCFu2bCEjI4MrV64AEBISgre3N++88w579uzB39+fffv2sXLlSkJDQ1m+fHm+/jt06MDSpUvp3bs3zZs3x97enq5du9K4cWPatGlD27ZtWbt2LW3btqVdu3akpaURHx9PSEgIK1asKNZ7CQ4OZty4cUyYMAFfX1+Cg4Px8vLizJkzHDx4kA0bNjBx4sRiJ5WvvfYaGRkZPPbYY9SvXx+j0ci+fftYvXo1JpOJGTNm3LZnqd/V6j9q+drrMdvEISIidz0llWXAx8eH5ORkZsyYQWxsLJ999hn29vbUqVOHdu3a0bt3b3NbFxcX1q5dy+jRo1m/fj2JiYn4+fmxaNEiateuXWBSOWvWLADWrl3LihUryMnJwdPTk8aNG2MwGIiPj2fkyJEkJCSwe/dumjZtSnx8PCdOnCh2Ugkwfvx42rVrx+zZs1mzZg3nzp3Dw8MDHx8fIiMjeeGFF4rd5+uvv85///tftm/fzsqVK8nOzqZOnTr07duXoUOH0rZt22L3We5ZM/Vc2Ajjkc1/jVSC5frK0o5BREQqNIPJZDLZOog7VXR0NOHh4cyfP9/i6TViW0FBQSQlJaGv7nUK2qiztD9k7IMu7+aOUKZtgm9Gwz0PwrOFbADTRh0RESkhJZU3kZdU5unTpw9LliyxYUQVl9FopFKlShZl+urewsUzsHwQHLru8Pz720OveVDVo/DrRERESkDT3zfRrFkzIiIizK91vI3t2NnZWfwupAiqekD/WJ1TKSIit4VGKqVUpKamEh0dfct2NWrUYPjw4WUej4iIiNxeSiqlVCQmJtK+fftbtvPy8iI1NbXsAxIREZHbSkmliIiIiFjNztYBiIiIiEj5p6RSRERERKympFJERERErKakUkRERESspnMqpcLIzs4mJyfH1mGIiIiUOzc+gKQgSiqlwjh69ChZWVm2DkNERKTcadiw4S3bKKmUCiMrKwt7e3scHPS1FxERKSqj0VikdvrXVSoUBweHIg3hi4iISPFoo46IiIiIWE1JpYiIiIhYTUmliIiIiFhNSaWIiIiIWE1JpYiIiIhYTUmliIiIiFhNRwqJyB3t8JnLHDufRd3qjvh4VLZ1OCIiUggllSJyR0rNvMy7646RfPxPc1nz+1wY3b4u3u5KLkVE7jRKKkXE5i5fy85X9uKivVRzrsT7zz9CKx83dhw+y5gVu3lx0V6+H9KkwH4qV7Iv61BFRKQQSipvIjo6mvDwcPPrPn36sGTJEvPrbdu28Z///Ieff/6ZzMxMAgMDSUxMtEGkdz+j0ZjvSTgmk8lG0Uhp6zj3lwLL3+7RmK5N6gDQtUkdTJgYuji50PabhzUvsxhFROTmlFQWQffu3WnWrBn+/v7msvPnzxMSEkJWVhb9+vXDw8MDb2/v2xKPwWC4KxPYrl278s033+Dk5MSVK1cs6uzs7IiIiAByk/20tDRbhCi3WSsfN4vXrX3cbRSJiIjcipLKIggNDSUsLMyibMeOHWRkZDB58mTeeOMN2wR2F/nss8/47rvvcHZ2LnAE0s7OjsjISAASExOVVN5l1rxiOZ2dmnmFQV/uZ8fhs+aRSoDthzMBmNenId7uzrc1RhERuTkllSV04sQJADw9PW0cSfl37NgxRo4cyfDhw1m+fDknT560dUhym924FvKh2lVpfp8L4+J2Y8JEax93th/O5K24PTS/z4WHale1UaQiIlIYnVNZAgaDgQEDBgAQHh6OwWDAYDBYTEenp6czYsQIfH19cXJyombNmvTq1Ys9e/bk62/dunUMHDiQRo0a4eLigouLCy1btuTjjz+2aJeYmIjBYAAgKSnJfF+DwUB0dDQAkZGR+WLJEx0dbdEWIDU1FYPBQFhYGHv37qVnz57UrFkTg8FAamqquV1cXBwdO3bEzc0NZ2dn/P39mTZtGtnZ+TdYFNegQYO45557mDhxotV9yd1jUhcffD2cGbo4mdaT1jB0cTK+Hs5M6uJj69BERKQAGqksgYiICHbt2kVcXJx5vSVgXlOZkpJCUFAQx48fp3PnzoSGhpKens7y5ctZtWoVa9asISAgwNzf1KlTOXjwIG3atKFHjx6cO3eO7777jpdffpl9+/Yxffp0c/8RERFERUXh5eVlMSWfF0NJ5d3fz8+PAQMGkJmZiaOjIwBjxoxh8uTJ1K1bl169euHq6sr69esZPXo027Zt46uvvirxfT/++GO+//571q1bR+XKOiZG/lKjsgMzQ311TqWISDmhpLIEIiMjiY6OJi4ursD1lv379+fkyZOsWrWKTp06mcvHjh1Ly5YtGTx4ML/88tfu1blz5+LjYzn6YjQa6dKlC7NmzeKf//wn9evXx9vbm8jISKKiosx/Ly2bNm1i3LhxjB8/3qL8+++/Z/LkyTz99NMsW7aMKlWqALk7r1999VU+/PBDli9fTq9evYp9z7S0NEaNGsWQIUMIDAwslfchdx8fj8pKJkVEygFNf5ey5ORkNm/ezIABAywSSoCGDRsyePBgdu/ebTENfmNCCeDg4MCQIUPIzs5m3bp1ZR63p6cnY8eOzVf+3nvvAfDRRx+ZE0rIXQIwZcoUDAYDMTExxb6fyWRi0KBBuLm5MXXq1JIHLiIiIncEjVSWsq1btwJw8uTJAkcS9+7da/4z74iiCxcuMG3aNGJjY0lJSeHixYsW1+RtCipLTZs2NU93X2/r1q1UrVqVefPmFXhd5cqVze+pOObOncuaNWv49ttvqVatWrGvFxERkTuLkspSlpmZe+RJQkICCQkJhbbLSxyzsrIICgpi586dNG/e3HzmpYODA6mpqSxYsICrV6+Wedy1a9cusDwzMxOj0UhUVFSh196YBN/K8ePH+fe//01YWBjBwcHFulYqLoezB7E/f4Ts6vUxuvnaOhwREbmBkspS5urqCsCcOXMYOnToLdvHxcWxc+dOXnrpJT755BOLuiVLlrBgwYJi3d/OLndFg9FozFd3/vz5Qq/L21V+I1dXVwwGA6dPny5WHDdz4MAB/vzzT6Kjoy12ohcUz9mzZ6lRo0ap3VvKF7vLmRiunKPGxkicft9hLr9apxXnHo8k2+1+G0YnIiLXU1JZyvJ2dW/ZsqVISWVKSgoA3bp1y1e3YcOGAq+xs7Mr9CgfN7fcJ5AcP348X11ycvIt47lRQEAA3377LQcOHKBBgwbFvr4gderUYdCgQQXWffnll1y+fNm8+cnJyalU7il3NsO1SwWWey58DAx24OQKz0RD/UfhyGacvh5O7WUh/B62o8Dr8pgqVblpvYiIlB4llaWsdevWBAQEEBMTQ7du3ejTp49FfU5ODhs2bDDvdvby8gJg48aNhISEmNslJSXlG7nM4+7uzrFjxwqsa9myJQCff/45/fr1M49cbtmyhUWLFhX7/QwbNoxvv/2WgQMHEhsbi4eHh0X9yZMnOXv2LA899FCR+2zUqBGffvppgXU//PADJ0+eLLRe7k515rcovNKUAyEzwa9H7mu/HmAywbLwm18HnPj7b6UXpIiI3JSSyjIQExND+/bt6du3LzNnzqRFixY4Oztz5MgRtmzZQkZGhvnZ1iEhIXh7e/POO++wZ88e/P392bdvHytXriQ0NJTly5fn679Dhw4sXbqU3r1707x5c+zt7enatSuNGzemTZs2tG3blrVr19K2bVvatWtHWloa8fHxhISEsGLFimK9l+DgYMaNG8eECRPw9fUlODgYLy8vzpw5w8GDB9mwYQMTJ04sVlIpUmz1H7V87fWYbeIQEZFCKaksAz4+PiQnJzNjxgxiY2P57LPPsLe3p06dOrRr147evXub27q4uLB27VpGjx7N+vXrSUxMxM/Pj0WLFlG7du0Ck8pZs2YBsHbtWlasWEFOTg6enp40btwYg8FAfHw8I0eOJCEhgd27d9O0aVPi4+M5ceJEsZNKgPHjx9OuXTtmz57NmjVrOHfuHB4eHvj4+BAZGckLL7xQ8g9LBPg9/KcCy80jkUc2/zVSCZC2CYCMHl9hrKF1lSIidwKDyWQy2TqIO1V0dDTh4eHMnz8/3wHnYjtBQUEkJSVR3K/u/v37cXJyolKlSmUUmZQ2u8uZuP0wHMfzhzB0mZY7Qpm2CdM3o8iq/gBnQoq3kU1ERIrv2rVr5uV6N6PDz4sg7/neffv2tXUoFZbRaDQ/5zwpKcnW4chtklPZnbOdZpHl3giWhcP0hrAsnCz3RpztNNPW4YmIyHU0/X0TzZo1IyIiwvw677Byuf3s7OwsfhdSceQ4u3GmyzydUykicofT9LeUitTU1ELPnLxejRo1GD58eJnHUxBNf4uIiBRfUae/lVRKqUhMTKR9+/a3bOfl5UVqamrZB1QAJZUiIiLFV9SkUtPfUiqCgoKKvXFGRERE7h7aqCMiIiIiVlNSKSIiIiJWU1IpIiIiIlZTUikiIiIiVtNGHalQjEajrUMQEREpV4r6b6eSSqkwHB0dycrKIjs729ahiIiI3HV0TqVUGNnZ2eTk5Ng6DBERkXKnKGc8K6kUEREREatpo46IiIiIWE1JpYiIiIhYTUmliIiIiFhNSaWIiIiIWE1JpYiIiIhYTUmliIiIiFhNSaWIiIiIWE1P1BGpwA6cukDqmUt4e1ShQe1qtg5HRETKMSWVIhVQ5sUshsUks/HgaXPZ4741mf1cc9yrOtowMhERKa/0RB2RCujFT7fx2+9/ML67P6183Nhx+Cxvxe3h4XtdWTgowNbhiYhIOaQ1lTcRHR2NwWAw//Tt29eiftu2bXTo0AEPDw8MBgNBQUG2CbQCMBqNFr8Lg8Fg65DKrQOnLrDx4GnGd/ena5M61KrmTNcmdYjq7seGA6c5cOqCrUMUEZFySNPfRdC9e3eaNWuGv7+/uez8+fOEhISQlZVFv3798PDwwNvb+7bEYzAYCAwMJDEx8bbcryykpqbi4+NTaH1MTIxFEm9nZ0dERASQm+ynpaWVeYx3q9QzlwBo5eNmUd7ax91cr/WVIiJSXEoqiyA0NJSwsDCLsh07dpCRkcHkyZN54403bBPYXaBp06aEhobmK78+gYfcpDIyMhKAxMREJZVW8PaoAsCOw2fp2qSOuXz74UyLehERkeJQUllCJ06cAMDT09PGkZRvzZo1MyeLcns0qF2Nx31r8lbcHkyYaO3jzvbDmUTE/coTDWpqlFJEREpEaypLwGAwMGDAAADCw8PNa/yun45OT09nxIgR+Pr64uTkRM2aNenVqxd79uzJ19+6desYOHAgjRo1wsXFBRcXF1q2bMnHH39s0S4xMdG8ljApKclifWF0dDQAkZGR+WLJk7dGNK8t5E5DGwwGwsLC2Lt3Lz179qRmzZoYDAZSU1PN7eLi4ujYsSNubm44Ozvj7+/PtGnTyM7OLtmHKDY1+7nmPHyvK0MXJ9N60hqGLk7m4XtdmdW3ua1DExGRckojlSUQERHBrl27iIuLM6+3BMxrKlNSUggKCuL48eN07tyZ0NBQ0tPTWb58OatWrWLNmjUEBPy1w3bq1KkcPHiQNm3a0KNHD86dO8d3333Hyy+/zL59+5g+fbq5/4iICKKiovDy8rKYks+LoaTy7u/n58eAAQPIzMzE0TH3aJkxY8YwefJk6tatS69evXB1dWX9+vWMHj2abdu28dVXX5X4vidOnGDu3LmcO3eOe++9l44dO1K3bl2r3ovcmntVRxYOCtA5lSIiUnpMUqj58+ebANP8+fOLVffoo4+aHBwcTKtXr7Yo37dvn6latWqmxo0bW5QfOnQoXx/Xrl0zderUyWRvb29KS0uzqANMgYGBBcYcERFhAkzr1q0rUsyHDx82ASbANG7cuHzXrF692gSYnn76adPFixfN5Tk5OaYhQ4aYANOyZcsKjOVmrr/v9T8ODg6mkSNHmrKzswu9NjAw0KSvroiIyJ1F09+lLDk5mc2bNzNgwAA6depkUdewYUMGDx7M7t27LabBC9oF7eDgwJAhQ8jOzmbdunVlHrenpydjx47NV/7ee+8B8NFHH1Glyl8bOAwGA1OmTMFgMBATE1Ps+1WpUsU84vvHH3+Qnp5OfHw8DRo0YMaMGbz55pslfzMiIiJy22n6u5Rt3boVgJMnTxa4AWXv3r3mP/N2OF+4cIFp06YRGxtLSkoKFy9etLgmb1NQWWratKl5uvt6W7dupWrVqsybN6/A6ypXrmx+T8VRq1Yti8+nWrVqhISE0KpVK/z9/ZkxYwb/+te/cHNzK7wTERERuWMoqSxlmZm5x7IkJCSQkJBQaLu8xDErK4ugoCB27txJ8+bNzWdeOjg4kJqayoIFC7h69WqZx127du0CyzMzMzEajURFRRV67Y1JsDU8PT3p0qULCxcuZMeOHXTu3LnU+pYbpO+FzEPgfj/UetDW0YiISDmnpLKUubq6AjBnzhyGDh16y/ZxcXHs3LmTl156iU8++cSibsmSJSxYsKBY97ezy13RYDQa89WdP3++0OsKe0KNq6srBoOB06dPF1hfFmrWrAnApUuXbts9K4yLp+FSJqwcDmmb/ir3egz+NhPuaWiryEREpJzTmspSlrere8uWLUVqn5KSAkC3bt3y1W3YsKHAa+zs7Ao9yidvuvj48eP56pKTk4sU0/UCAgI4c+YMBw4cKPa1JbV9+3aA2/aEortS1sWCf959AD4IgFO/wjPR8Pr+3D9P/ZpbXth1eT8iIiKF0EhlKWvdujUBAQHExMTQrVs3+vTpY1Gfk5PDhg0bCAwMBMDLywuAjRs3EhISYm6XlJSUb+Qyj7u7O8eOHSuwrmXLlgB8/vnn9OvXzzxyuWXLFhYtWlTs9zNs2DC+/fZbBg4cSGxsLB4eHhb1J0+e5OzZszz00EPF6nf79u00b96cSpUqWZTPmDGDTZs28fDDD9O0adNixyv/39v3Fl5nyoGQmeDXI/e1Xw8wmWBZ+M2vA4gsfLRbREQqNiWVZSAmJob27dvTt29fZs6cSYsWLXB2dubIkSNs2bKFjIwMrly5AkBISAje3t6888477NmzB39/f/bt28fKlSsJDQ1l+fLl+frv0KEDS5cupXfv3jRv3hx7e3u6du1K48aNadOmDW3btmXt2rW0bduWdu3akZaWRnx8PCEhIaxYsaJY7yU4OJhx48YxYcIEfH19CQ4OxsvLizNnznDw4EE2bNjAxIkTi51U/utf/2Lv3r0EBgZSr149Ll++zJYtW0hOTsbNzY2FCxcWOiUvpaD+o5avvR6zTRwiInLXUFJZBnx8fEhOTmbGjBnExsby2WefYW9vT506dWjXrh29e/c2t3VxcWHt2rWMHj2a9evXk5iYiJ+fH4sWLaJ27doFJpWzZs0CYO3ataxYsYKcnBw8PT1p3LgxBoOB+Ph4Ro4cSUJCArt376Zp06bEx8dz4sSJYieVAOPHj6ddu3bMnj2bNWvWcO7cOTw8PPDx8SEyMpIXXnih2H2++OKLLF++nM2bN5vXa3p5efHPf/6TUaNG6QB0a40p5MSAvJHII5v/GqmEv9ZXDk7UukoRESkRg8lkMtk6iDtVdHQ04eHhzJ8/3+LpNWJbQUFBJCUloa9uCVw8DUv7Q8Y+6PJu7ghl2ib4ZjTc8yCEF35igYiIyM0oqbyJvKQyT58+fViyZIkNI6q4jEZjvvWX+uqW0MUzsHwQHLruUP3720OveVDVo/DrREREbkLT3zfRrFkzIiIizK/zDiuX28/Ozs7idyFWqOoB/WN1TqWIiJQqjVRKqUhNTSU6OvqW7WrUqMHw4cPLPB4RERG5vZRUSqlITEykffv2t2zn5eVFampq2QckIiIit5WSShERERGxmp6oIyIiIiJWU1IpIiIiIlZTUikiIiIiVlNSKSIiIiJW0zmVUmFkZ2eTk5Nj6zBERETKnRsfQFIQJZVSYRw9epSsrCxbhyEiIlLuNGzY8JZtlFRKhZGVlYW9vT0ODvrai4iIFJXRaCxSO/3rKhWKg4NDkYbwRUREpHi0UUdERERErKakUkRERESspqRSRERERKympFJERERErKakUkRERESspqRSRERERKymI4VEKrDDZy5z7HwWdas74uNR2dbhiIhIOaakUqQCOnfZSMR3qew4esFc1qpeNaKCvalRWf9ZEBGR4tP0t0gFFPFdKimZV3j/+UfY/mZH3n/+EVIyrxC5KtXWoYmISDmlpPImoqOjMRgM5p++ffta1G/bto0OHTrg4eGBwWAgKCjINoFWAEaj0eJ3YTAYbB1SuXX4zGV2HL3AhO6N6dqkDrWqOdO1SR3Gd/dn+5ELHD5z2dYhiohIOaR5riLo3r07zZo1w9/f31x2/vx5QkJCyMrKol+/fnh4eODt7X1b4jEYDAQGBpKYmHhb7leWsrKyeO+991i8eDH79u0DwMvLi8DAQN5//31zOzs7OyIiIoDcZD8tLc0m8d4Njp3PAqCVj5tFeWsfd3O91leKiEhxKaksgtDQUMLCwizKduzYQUZGBpMnT+aNN96wTWDl3NmzZwkODmb79u08+uijvPzyywAcPnyYL7/8Ml9SGRkZCUBiYqKSSivUre4IwI7DZ+napI65fPvhTIt6ERGR4lBSWUInTpwAwNPT08aRlF+DBg1ix44dLFq0iOeff96izmg02iiqu5+PR2Va1avGuLjdmDDR2sed7YczeStuD63rV9MopYiIlIjWVJaAwWBgwIABAISHh5vX+F0/HZ2ens6IESPw9fXFycmJmjVr0qtXL/bs2ZOvv3Xr1jFw4EAaNWqEi4sLLi4utGzZko8//tiiXWJionktYVJSksX6wujoaAAiIyPzxZInb41oXluA1NRUDAYDYWFh7N27l549e1KzZk0MBgOpqanmdnFxcXTs2BE3NzecnZ3x9/dn2rRpZGdnl+gz3LZtGytWrODFF1/Ml1ACODjo/++Upahgb3w9nBm6OJnWk9YwdHEyvh7ORD7lbevQRESknNK/3CUQERHBrl27iIuLM6+3BMxrKlNSUggKCuL48eN07tyZ0NBQ0tPTWb58OatWrWLNmjUEBASY+5s6dSoHDx6kTZs29OjRg3PnzvHdd9/x8ssvs2/fPqZPn27uPyIigqioKLy8vCym5PNiKKm8+/v5+TFgwAAyMzNxdMydBh0zZgyTJ0+mbt269OrVC1dXV9avX8/o0aPZtm0bX331VbHv9+WXXwLwzDPPcPr0aeLj4zl16hT16tXj6aefxsPDw6r3IzdXo7IDM0N9dU6liIiUGiWVJRAZGUl0dDRxcXEFrrfs378/J0+eZNWqVXTq1MlcPnbsWFq2bMngwYP55ZdfzOVz587Fx8fHog+j0UiXLl2YNWsW//znP6lfvz7e3t5ERkYSFRVl/ntp2bRpE+PGjWP8+PEW5d9//z2TJ0/m6aefZtmyZVSpUgUAk8nEq6++yocffsjy5cvp1atXse73448/ArnJbL9+/Th//ry5zsXFhU8//ZQ+ffpY+a7kVnw8KiuZFBGRUqHp71KWnJzM5s2bGTBggEVCCdCwYUMGDx7M7t27LabBb0woIXf6d8iQIWRnZ7Nu3boyj9vT05OxY8fmK3/vvfcA+Oijj8wJJeQuAZgyZQoGg4GYmJhi3y89PR2A0aNH0717d1JSUjh79ixffPEFdnZ29OvXzyLxFhERkTubRipL2datWwE4efJkgSOJe/fuNf+Zd0TRhQsXmDZtGrGxsaSkpHDx4kWLa/I2BZWlpk2bmqe7r7d161aqVq3KvHnzCryucuXK5vdUHDk5OQA0adLEvNYT4IUXXuDChQu88sorzJ49m08//bTYfYuIiMjtp6SylGVm5h7LkpCQQEJCQqHt8hLHrKwsgoKC2LlzJ82bNzefeeng4EBqaioLFizg6tWrZR537dq1CyzPzMzEaDQSFRVV6LU3JsFFUb16dQD+9re/5TvIPCQkhFdeecU8RS5lz+HsQezPHyG7en2Mbr62DkdERMohJZWlzNXVFYA5c+YwdOjQW7aPi4tj586dvPTSS3zyyScWdUuWLGHBggXFur+dXe6KhoKO5Ll+3eKNCntCjaurKwaDgdOnTxcrjltp1KgRP/74IzVq1MhXl1d2+bKe7FKW7C5nYrhyjhobI3H6fYe5/GqdVpx7PJJst/ttGJ2IiJQ3WlNZyvJ2dW/ZsqVI7VNSUgDo1q1bvroNGzYUeI2dnV2hR/m4ueU+JeX48eP56pKTk4sU0/UCAgI4c+YMBw4cKPa1N9OhQwcA/ve//+Wryyu7XU8oulsZrl266Y/nwseovSwEp7MH4JloeH0/PBON09kD1F4WctNrRUREbqSRylLWunVrAgICiImJoVu3bvl2MOfk5LBhwwYCAwOB3EcSAmzcuJGQkBBzu6SkpHwjl3nc3d05duxYgXUtW7YE4PPPP6dfv37mkcstW7awaNGiYr+fYcOG8e233zJw4EBiY2PzHfVz8uRJzp49y0MPPVSsfnv37s2///1vFi1axD//+U8aN24M5C4HyHsc47PPPlvseOUvdea3uHUjUw6EzAS/Hrmv/XqAyQTLwm96/Ym//1Y6QYqIyF1DSWUZiImJoX379vTt25eZM2fSokULnJ2dOXLkCFu2bCEjI4MrV64AuesHvb29eeedd9izZw/+/v7s27ePlStXEhoayvLly/P136FDB5YuXUrv3r1p3rw59vb2dO3alcaNG9OmTRvatm3L2rVradu2Le3atSMtLY34+HhCQkJYsWJFsd5LcHAw48aNY8KECfj6+hIcHIyXlxdnzpzh4MGDbNiwgYkTJxY7qXR1deWTTz6hd+/etGnTht69e+Pm5sYPP/zAr7/+SpcuXfId1SRlpP6jlq+9HrNNHCIiUq4pqSwDPj4+JCcnM2PGDGJjY/nss8+wt7enTp06tGvXjt69e5vburi4sHbtWkaPHs369etJTEzEz8+PRYsWUbt27QKTylmzZgGwdu1aVqxYQU5ODp6enjRu3BiDwUB8fDwjR44kISGB3bt307RpU+Lj4zlx4kSxk0qA8ePH065dO2bPns2aNWs4d+4cHh4e+Pj4EBkZyQsvvFCizyk0NJSkpCQmTpxIfHw8ly5dokGDBkydOpWRI0dib29fon4l1+/hP9203jwSeWTzXyOVAGmbAMjo8RXGGlpXKSIiRWMwmUwmWwdxp4qOjiY8PJz58+dr1OwOEhQURFJSEsX96u7fvx8nJycqVapURpGVL3aXM3H7YTiO5w9h6DItd4QybROmb0aRVf0BzoQUb5OYiIjcna5du2Zerncz2qhTBHnP9+7bt6+tQ6mwjEaj+TnnSUlJtg7nrpBT2Z2znWaR5d4IloXD9IawLJws90ac7TTT1uGJiEg5o+nvm2jWrJl50whgPqxcbj87OzuL34WUjhxnN850madzKkVExGqa/pZSkZqaSnR09C3b1ahRg+HDh5d5PAXR9LeIiEjxFXX6W0mllIrExETat29/y3ZeXl6kpqaWfUAFUFIpIiJSfEVNKjX9LaUiKCio2BtnRERE5O6hjToiIiIiYjUllSIiIiJiNSWVIiIiImI1JZUiIiIiYjVt1JEKxWg02joEERGRcqWo/3YqqZQKw9HRkaysLLKzs20dioiIyF1H51RKhZGdnU1OTo6twxARESl3inLGs5JKEREREbGaNuqIiIiIiNWUVIqIiIiI1ZRUioiIiIjVlFSKiIiIiNWUVIqIiIiI1ZRUioiIiIjVdPi5SAV24NQFUs9cwtujCg1qV7N1OCIiUo4pqRSpgDIvZjEsJpmNB0+byx73rcns55rjXtXRhpGJiEh5pcPPRSqYS1lGBkX/yP5TFxjf3Z9WPm7sOHyWt+L28PC9riwcFGDrEEVEpBzSmsqbiI6OxmAwmH/69u1rUb9t2zY6dOiAh4cHBoOBoKAg2wRaARiNRovfhcFgsHVI5dbDb61iy6EzjO/uT9cmdahVzZmuTeoQ1d2PDQdOc+DUBVuHKCIi5ZCmv4uge/fuNGvWDH9/f3PZ+fPnCQkJISsri379+uHh4YG3t/dticdgMBAYGEhiYuJtuV9ZCAsLY8GCBTdtM378eMaNGweAnZ0dERERQG6yn5aWVuYx3u1a+bhZvG7t4w5A6plLWl8pIiLFpqSyCEJDQwkLC7Mo27FjBxkZGUyePJk33njDNoGVY6GhoYUm4dOmTePixYs89dRT5jI7OzsiIyMBSExMVFJphfihj9HtvU3sOHyWrk3qmMu3H84EwNujiq1CExGRckxJZQmdOHECAE9PTxtHUj6FhoYSGhqar/ynn34iKiqKxo0b07p169sfWAXQpG4NHvetyVtxezBhorWPO9sPZxIR9ytPNKipUUoRESkRraksAYPBwIABAwAIDw83r/G7fjo6PT2dESNG4Ovri5OTEzVr1qRXr17s2bMnX3/r1q1j4MCBNGrUCBcXF1xcXGjZsiUff/yxRbvExETzWsKkpCSL9YXR0dEAREZG5oslT94a0by2AKmpqRgMBsLCwti7dy89e/akZs2aGAwGUlNTze3i4uLo2LEjbm5uODs74+/vz7Rp08jOzi7Zh1iITz/9FIBBgwaVar9iafZzzXn4XleGLk6m9aQ1DF2czMP3ujKrb3NbhyYiIuWURipLICIigl27dhEXF2debwmYp3NTUlIICgri+PHjdO7cmdDQUNLT01m+fDmrVq1izZo1BAT8tcN26tSpHDx4kDZt2tCjRw/OnTvHd999x8svv8y+ffuYPn26uf+IiAiioqLw8vKymJLPi6Gk8u7v5+fHgAEDyMzMxNEx92iZMWPGMHnyZOrWrUuvXr1wdXVl/fr1jB49mm3btvHVV19Zde88ly9fJiYmBicnJ/r161cqfUrB3Ks6snBQgM6pFBGR0mOSQs2fP98EmObPn1+sukcffdTk4OBgWr16tUX5vn37TNWqVTM1btzYovzQoUP5+rh27ZqpU6dOJnt7e1NaWppFHWAKDAwsMOaIiAgTYFq3bl2RYj58+LAJMAGmcePG5btm9erVJsD09NNPmy5evGguz8nJMQ0ZMsQEmJYtW1ZgLMX1+eefmwBT3759b9ouMDDQpK+uiIjInUXT36UsOTmZzZs3M2DAADp16mRR17BhQwYPHszu3bstpsF9fHzy9ePg4MCQIUPIzs5m3bp1ZR63p6cnY8eOzVf+3nvvAfDRRx9RpcpfGzgMBgNTpkzBYDAQExNTKjHMmzcPgJdeeqlU+hMREZHbR9PfpWzr1q0AnDx50rxb+Xp79+41/5l3RNGFCxeYNm0asbGxpKSkcPHiRYtr8jYFlaWmTZuap7uvt3XrVqpWrWpO+G5UuXJl83uyxsGDB1m/fj0+Pj506NDB6v5ERETk9lJSWcoyM3OPZUlISCAhIaHQdnmJY1ZWFkFBQezcuZPmzZubz7x0cHAgNTWVBQsWcPXq1TKPu3bt2gWWZ2ZmYjQaiYqKKvTaG5Pgkpg3bx4mk4mBAwfqYPOylL4XMg+B+/1Q60FbRyMiIncRJZWlzNXVFYA5c+YwdOjQW7aPi4tj586dvPTSS3zyyScWdUuWLLnlAeE3srPLXdFgNBrz1Z0/f77Q6wpL5FxdXTEYDJw+fbrA+tKQnZ3NggULsLe3Jzw8vMzuU2FdPA2XMmHlcEjb9Fe512Pw7EKo6mGz0ERE5O6hNZWlLG9X95YtW4rUPiUlBYBu3brlq9uwYUOB19jZ2RV6lI+bW+5TUo4fP56vLjk5uUgxXS8gIIAzZ85w4MCBYl9bVN988w2///47wcHB3HfffWV2n7te1sWCf959AD4IgFO/wjPR8Pr+3D9P/QrLwgq/Lsv6EWgREak4NFJZylq3bk1AQAAxMTF069aNPn36WNTn5OSwYcMGAgMDAfDy8gJg48aNhISEmNslJSXlG7nM4+7uzrFjxwqsa9myJQCff/45/fr1M49cbtmyhUWLFhX7/QwbNoxvv/2WgQMHEhsbi4eH5ajWyZMnOXv2LA899FCx+86Tt15TZ1Na6e17C68z5UDITPDrkfvarweYTLAs/ObXRRY+ui0iInI9JZVlICYmhvbt29O3b19mzpxJixYtcHZ25siRI2zZsoWMjAyuXLkCQEhICN7e3rzzzjvs2bMHf39/9u3bx8qVKwkNDWX58uX5+u/QoQNLly6ld+/eNG/eHHt7e7p27Urjxo1p06YNbdu2Ze3atbRt25Z27dqRlpZGfHw8ISEhrFixoljvJTg4mHHjxjFhwgR8fX0JDg7Gy8uLM2fOcPDgQTZs2MDEiRNLnFSeOnWKhIQEateubZFUSxmo/6jla6/HbBOHiIjclZRUlgEfHx+Sk5OZMWMGsbGxfPbZZ9jb21OnTh3atWtH7969zW1dXFxYu3Yto0ePZv369SQmJuLn58eiRYuoXbt2gUnlrFmzAFi7di0rVqwgJycHT09PGjdujMFgID4+npEjR5KQkMDu3btp2rQp8fHxnDhxothJJcD48eNp164ds2fPZs2aNZw7dw4PDw98fHyIjIzkhRdeKPFntWDBAoxGIwMGDMDBQV9Hq4wp5JSAvJHII5v/GqmEv9ZXDk6EexqWaWgiInL3M5hMJpOtg7hTRUdHEx4ezvz58y2eXiO2FRQURFJSEvrqFtHF07C0P2Tsgy7v5o5Qpm2Cb0aDZ2PoH2vrCEVE5C6gpPIm8pLKPH369GHJkiU2jKjiMhqNVKpUyaJMX91iuHgGlg+CQ9cdpH9/e+g1T7u/RUSkVGi+8SaaNWtGRESE+XXeYeVy+9nZ2Vn8LqSYqnrkjkjqnEoRESkjGqmUUpGamkp0dPQt29WoUYPhw4eXeTwiIiJyeymplFKRmJhI+/btb9nOy8uL1NTUsg9IREREbisllSIiIiJiNT1RR0RERESspqRSRERERKympFJERERErKakUkRERESspqRSRERERKymw8+lwsjOziYnJ8fWYYiIiJQ7Nz7VriBKKqXCOHr0KFlZWbYOQ0REpNxp2LDhLdsoqZQKIysrC3t7exwc9LUXEREpKqPRWKR2+tdVKhQHB4ciDeGLiIhI8WijjoiIiIhYTUmliIiIiFhNSaWIiIiIWE1JpYiIiIhYTUmliIiIiFhNSaWIiIiIWE1HColUYIfPXObY+SzqVnfEx6OyrcMREZFyTEmlSAV07rKRiO9S2XH0grmsVb1qRAV7U6Oy/rMgIiLFp+lvkQrm8rVsxn17mJTMK7z//CNsf7Mj7z//CCmZV4hclWrr8EREpJxSUnkT0dHRGAwG80/fvn0t6rdt20aHDh3w8PDAYDAQFBRkm0ArAKPRaPG7MBgMtg6p3Oo49xd+OvYnE7o3pmuTOtSq5kzXJnUY392f7UcucPjMZVuHKCIi5ZDmuYqge/fuNGvWDH9/f3PZ+fPnCQkJISsri379+uHh4YG3t/dticdgMBAYGEhiYuJtuV9ZuXz5MnPnzuWLL77g8OHDmEwmvLy8ePbZZxk6dCjVq1c3t7WzsyMiIgLITfbT0tJsFfZdo5WPm8Xr1j7uABw7n6X1lSIiUmxKKosgNDSUsLAwi7IdO3aQkZHB5MmTeeONN2wTWDl27do12rdvz7Zt22jWrBkDBgzAYDCwbt06xo4dS0xMDNu3b6dKlSpAblIZGRkJQGJiopJKK8zr05BBX+5nx+GzdG1Sx1y+/XAmAHWrO9oqNBERKceUVJbQiRMnAPD09LRxJOXTihUr2LZtGz179mT58uUWdT169CA2NpZly5bRv39/G0V493qodlVa1avGuLjdmDDR2sed7YczeStuD63rV9MopYiIlIjWVJaAwWBgwIABAISHh5vX+F0/HZ2ens6IESPw9fXFycmJmjVr0qtXL/bs+X/t3XtclWW+///XDaiYiHKYxHICirQCUncm2oGDbs3DRhEsmUlTTHfObL+NOfrdbssBtLYdrK3V7KacEmuMfgojMJKHGRFEQ/MbuMOZLQqxMDVDQRhGMwTW7w8ea+UKEBBwiev9fDx44Lqu677vz70WysfrdB9pcr49e/Ywd+5chgwZgpubG25ubowYMYL33nvPpl12drZ1LmFOTo7N/MKkpCQAEhISmsRiYZkjamkLYDKZMAyDOXPmcPToUaKjo/H29sYwDEwmk7Vdeno6Y8eOxcPDA1dXV4KCglizZg319fXX9B5+9dVXAEycOLFJ3aRJk4DG91C6RuIEPwK8XFn4cQEjX9rNwo8LCPByJeExP3uHJiIi3ZR6Kq9BfHw8hw8fJj093TrfErDOqSwpKSE8PJxTp04xfvx4oqKiKC8vJzU1lZ07d7J7925CQkKs53vllVcoLi5m1KhRTJs2jaqqKnbs2MEzzzxDUVERr7/+uvX88fHxJCYm4uvrazMkb4nhWlmuHxgYyOzZs6msrKRnz8Zh0OXLl7N69WoGDRpETEwM7u7u7N27l6VLl3Lw4EG2bNnS7usFBgYCsGPHDubNm2dTt337di186mL9e7uwNipA+1SKiEinUVJ5DRISEkhKSiI9Pb3Z+ZZPPfUUZ86cYefOnYwbN85a/sILLzBixAjmz5/Pl19+aS1/55138Pf3tzlHXV0dkyZNYt26dfzqV7/ijjvuwM/Pj4SEBBITE61/7iz79+9nxYoVrFy50qb8z3/+M6tXr2bixImkpKRY5ziazWZ++ctf8rvf/Y7U1FRiYmLadb1/+Zd/ITIyktTUVB544AHCwsKAxt7Y4uJi/vu//5sRI0Z0zs1Ji/y9eiuZFBGRTqHh705WUFDAZ599xuzZs20SSoDBgwczf/58CgsLbYbBf5xQAri4uLBgwQLq6+vZs2dPl8ft4+PDCy+80KT87bffBuDdd9+1JpTQOAXg5ZdfxjAMkpOT2309wzDYunUrS5YsoaCggP/6r//iv/7rvygoKCAqKooJEyZc+82IiIjIdaeeyk524MABAM6cOdNsT+LRo0et3y1bFNXU1LBmzRrS0tIoKSnhwoULNsdYFgV1paFDh1qHu6904MAB+vTpw/vvv9/scb1797beU3t89913xMbGcvDgQT7++GPGjRuHYRjs3r2bZ599lu3bt3PgwAHuuuuudp9bRERErj8llZ2ssrJxW5bMzEwyMzNbbGdJHGtrawkPDyc/P5/hw4db97x0cXHBZDKxceNGvv/++y6Pe8CAAc2WV1ZWUldXR2JiYovH/jgJbovVq1eTkZFBeno6U6ZMsZY//vjj9O3bl4kTJ7Jy5Uo2btzY7nPLD1zOF+NcfYL6fndQ5xFg73BEROQmpqSyk7m7uwPw1ltvsXDhwlbbp6enk5+fz7x581i/fr1N3SeffNLupMrJqXFGQ11dXZO66urqFo9r6Qk17u7uGIbBuXPn2hVHaywJd0RERJO6iIgIDMPgiy++6NRrOgqn7yoxLlXRf18Cvb45ZC3/fuCDVD2SQL3HnXaMTkREblaaU9nJLKu68/Ly2tS+pKQEwKa3ziI3N7fZY5ycnFrcysfDo/EpKadOnWpSV1BQ0KaYrhQSEkJFRQXHjx9v97FXU1tbC8DZs2eb1J07dw6z2UyvXr069Zo3C+Pyxat++Xz0MANSIul1/jg8ngS/PgaPJ9Hr/HEGpERe9VgREZFrpZ7KTjZy5EhCQkJITk5mypQpzJgxw6a+oaGB3Nxc62pnX19fAPbt20dkZKS1XU5OTpOeSwtPT09OnjzZbJ1lxfSHH37IrFmzrD2XeXl5bNq0qd33Y5nfOHfuXNLS0vDy8rKpP3PmDOfPn+fee+9t13kffvhhjhw5QmJiIh988AHOzs5A4/vzm9/8Bmi+F1Ng4IYHWm9kboDItRA4rfF14DQwmyEl7qrHn/7X/+2cIEVExOEoqewCycnJREREEBsby9q1a3nggQdwdXXlxIkT5OXlcfbsWS5dugRAZGQkfn5+vPrqqxw5coSgoCCKiorYtm0bUVFRTZ42AzBmzBg2b97M9OnTGT58OM7OzkyePJng4GBGjRrF6NGjycrKYvTo0YSGhlJWVkZGRgaRkZFs3bq1XfcyYcIEVqxYwapVqwgICGDChAn4+vpSUVFBcXExubm5vPjii+1OKp9//nkyMjL48MMP+eKLLxgzZoz1MY2FhYX4+fnx7//+7+06p/zIHQ/ZvvZ92D5xiIiIQ1BS2QX8/f0pKCjgjTfeIC0tzdoTN3DgQEJDQ5k+fbq1rZubG1lZWSxdupS9e/eSnZ1NYGAgmzZtYsCAAc0mlevWrQMgKyuLrVu30tDQgI+PD8HBwRiGQUZGBosXLyYzM5PCwkKGDh1KRkYGp0+fbndSCbBy5UpCQ0N588032b17N1VVVXh5eeHv709CQgJPPvlku8/505/+lPz8fP7zP/+T7du38+6772IYBr6+vixevJjly5c36RWVRt/EXX2uqbUn8sRnP/RUApTtB+DstC3U9de8ShER6VyG2Ww22zuIG1VSUhJxcXFs2LChyQbnYj/h4eHk5OTQ3h/dY8eO0atXL3r06NFFkd0YnL6rxOMvi+hZ/RXGpDWNPZRl+zF/uoTafndREakV9SIi0naXL1+2Tte7Gi3UaQPL871jY2PtHYrDqqursz7nPCcnx97h3NAaentyftw6aj2HQEocvD4YUuKo9RzC+XFr7R2eiIjcpDT8fRXDhg0jPj7e+tqyWblcf05OTjafhVxdg6sHFZPe1z6VIiJy3Wj4WzqFyWQiKSmp1Xb9+/dn0aJFXR5Pcxxl+FtERKQztXX4W0mldIrs7Ow2bQHk6+uLyWTq+oCaoaRSRESk/dqaVGr4WzpFeHh4uxfOiIiIyM1DC3VEREREpMOUVIqIiIhIhympFBEREZEOU1IpIiIiIh2mhTriUOrq6uwdgoiISLfS1t+dSirFYfTs2ZPa2lrq6+vtHYqIiMhNR/tUisOor6+noaHB3mGIiIh0O23Z41lJpYiIiIh0mBbqiIiIiEiHKakUERERkQ5TUikiIiIiHaakUkREREQ6TEmliIiIiHSYkkoRERER6TBtfi7iwI5/W4Op4iJ+Xrdw94C+9g5HRES6MSWVIg6o8kItzyYXsK/4nLXskQBv3vzZcDz79LRjZCIi0l1p83MRBzTz9wf532/+zsqpQTzo78Gh0vP8Jv0I993mzkdPh9g7PBER6YY0p/IqkpKSMAzD+hUbG2tTf/DgQcaMGYOXlxeGYRAeHm6fQB1AXV2dzWdhGIa9Q+q2jn9bw77ic6ycGsTk+wdya19XJt8/kMSpgeQeP8fxb2vsHaKIiHRDGv5ug6lTpzJs2DCCgoKsZdXV1URGRlJbW8usWbPw8vLCz8/vusRjGAZhYWFkZ2dfl+t1lfPnz/PSSy+RlpbG119/jbu7O2FhYSQmJhIYGGjT1snJifj4eKAx2S8rK7NHyDcFU8VFAB7097ApH+nvaa3X/EoREWkvJZVtEBUVxZw5c2zKDh06xNmzZ1m9ejXLli2zT2DdWEVFBaNHj+b48eOMHj2aqVOn8s0335Camsr27dvJysoiJOSHYVgnJycSEhIAyM7OVlLZAX5etwBwqPQ8k+8faC3/vLTSpl5ERKQ9lFReo9OnTwPg4+Nj50i6p/j4eI4fP87ixYt5/fXXreV5eXk8+uijzJ07l8LCQpycNEOjs909oC+PBHjzm/QjmDEz0t+Tz0sriU//K4/e7a1eShERuSb6jX0NDMNg9uzZAMTFxVnn+F05HF1eXs5zzz1HQEAAvXr1wtvbm5iYGI4cOdLkfHv27GHu3LkMGTIENzc33NzcGDFiBO+9955Nu+zsbOtcwpycHJv5hUlJSQAkJCQ0icXCMkfU0hbAZDJhGAZz5szh6NGjREdH4+3tjWEYmEwma7v09HTGjh2Lh4cHrq6uBAUFsWbNGurr66/pPUxLS8PJyYnExESb8tGjRxMZGcnf/vY3cnJyrunc0ro3fzac+25zZ+HHBYx8aTcLPy7gvtvcWRc73N6hiYhIN6WeymsQHx/P4cOHSU9Pt863BKxzKktKSggPD+fUqVOMHz+eqKgoysvLSU1NZefOnezevdtmaPeVV16huLiYUaNGMW3aNKqqqtixYwfPPPMMRUVF1p48Pz8/4uPjSUxMxNfX12ZI3hLDtbJcPzAwkNmzZ1NZWUnPno1byyxfvpzVq1czaNAgYmJicHd3Z+/evSxdupSDBw+yZcuWdl/v22+/xdvbGzc3tyZ1/v7+AGRlZREREdGh+5LmefbpyUdPh2ifShER6TxmadGGDRvMgHnDhg3tqnvooYfMLi4u5l27dtmUFxUVmfv27WsODg62Kf/qq6+anOPy5cvmcePGmZ2dnc1lZWU2dYA5LCys2Zjj4+PNgHnPnj1tirm0tNQMmAHzihUrmhyza9cuM2CeOHGi+cKFC9byhoYG84IFC8yAOSUlpdlYrmbgwIFmJycnc01NTZO6qKgoM2B+4oknmj02LCzMrB9dERGRG4uGvztZQUEBn332GbNnz2bcuHE2dYMHD2b+/PkUFhbaDINbeuau5OLiwoIFC6ivr2fPnj1dHrePjw8vvPBCk/K3334bgHfffZdbbvlhAYdhGLz88ssYhkFycnK7rzdx4kQaGhqaDH9//vnnbNu2DYCqqqp2n1dERETsQ8PfnezAgQMAnDlzxrpa+UpHjx61frdsUVRTU8OaNWtIS0ujpKSECxcu2BxjWRTUlYYOHWod7r7SgQMH6NOnD++//36zx/Xu3dt6T+2RmJjI9u3bWbNmDXl5eYwaNYpvvvmGlJQU7rvvPr788kucnZ3bfV4RERGxDyWVnayysnFblszMTDIzM1tsZ0kca2trCQ8PJz8/n+HDh1v3vHRxccFkMrFx40a+//77Lo97wIABzZZXVlZSV1fXpEfxSj9Ogtti0KBBHDp0iPj4eLZv387nn3/OT3/6U1auXImfnx+xsbH85Cc/afd5pYPKj0LlV+B5J9x6j72jERGRbkRJZSdzd3cH4K233mLhwoWttk9PTyc/P5958+axfv16m7pPPvmEjRs3tuv6li146urqmtRVV1e3eFxLT6hxd3fHMAzOnTvXbH1H3H777fz+979vUm7p4R0xYkSnX1OaceEcXKyEbYugbP8P5b4PwxMfQR8vu4UmIiLdh+ZUdjLLqu68vLw2tS8pKQFgypQpTepyc3ObPcbJyanFrXw8PBqfknLq1KkmdQUFBW2K6UohISFUVFRw/Pjxdh97Lerr6/nkk09wcXEhJibmulzzpld74epfr90F/x0C3/4VHk+CXx9r/P7tXyFlTsvHiYiIXEE9lZ1s5MiRhISEkJyczJQpU5gxY4ZNfUNDA7m5uYSFhQHg6+sLwL59+4iMjLS2y8nJadJzaeHp6cnJkyebrbP07n344YfMmjXL2nOZl5fHpk2b2n0/zz77LNu3b2fu3LmkpaXh5WXba3XmzBnOnz/Pvffe267zXr58mbq6Onr37m0ta2hoYMmSJRQVFfHcc89x2223tTteacZ/tuF9NDdA5FoInNb4OnAamM2QEtfy8Qkt93yLiIjjUVLZBZKTk4mIiCA2Npa1a9fywAMP4OrqyokTJ8jLy+Ps2bNcunQJgMjISPz8/Hj11Vc5cuQIQUFBFBUVsW3bNqKiokhNTW1y/jFjxrB582amT5/O8OHDcXZ2ZvLkyQQHBzNq1ChGjx5NVlYWo0ePJjQ0lLKyMjIyMoiMjGTr1q3tupcJEyawYsUKVq1aRUBAABMmTMDX15eKigqKi4vJzc3lxRdfbHdS+e233xIYGMj48ePx9/entraWnTt3cvToUSZPnszq1avbdT7pBHc8ZPva92H7xCEiIt2Sksou4O/vT0FBAW+88QZpaWl88MEHODs7M3DgQEJDQ5k+fbq1rZubG1lZWSxdupS9e/eSnZ1NYGAgmzZtYsCAAc0mlevWrQMaNwffunUrDQ0N+Pj4EBwcjGEYZGRksHjxYjIzMyksLGTo0KFkZGRw+vTpdieVACtXriQ0NJQ333yT3bt3U1VVhZeXF/7+/iQkJPDkk0+2+5z9+vVj6tSp7N+/n23bttGjRw+CgoJYv349c+fO1eMZO9PyVnYPsPREnvjsh55K+GF+5fxs+MngLglNRERuHobZbDbbO4gbVVJSEnFxcWzYsMHm6TViX+Hh4eTk5KAf3U5y4RxsfgrOFsGk1xp7KMv2w6dLwScYnkqzd4QiItINKKm8CktSaTFjxgw++eQTO0bkuOrq6ujRo4dNmX50O9GFCkh9Gr66YqP9OyMg5n2t/hYRkTbR8PdVDBs2jPj4eOtry2blcv05OTnZfBbSyfp4NfZIap9KERG5RuqplE5hMplISkpqtV3//v1ZtGhRl8cjIiIi15eSSukU2dnZREREtNrO19cXk8nU9QGJiIjIdaWkUkREREQ6TPu2iIiIiEiHKakUERERkQ5TUikiIiIiHaakUkREREQ6TPtUisOor6+noaHB3mGIiIh0Oz9+AElzlFSKw/j666+pra21dxgiIiLdzuDBg1tto6RSHEZtbS3Ozs64uOjHXkREpK3q6ura1E6/XcWhuLi4tKkLX0RERNpHC3VEREREpMOUVIqIiIhIhympFBEREZEOU1IpIiIiIh2mpFJEREREOkxJpYiIiIh0mJJKEREREekw7VMp4sBKK77jZHUtg/r1xN+rt73DERGRbkxJpYgDqvqujvgdJg59XWMte/CnfUmc4Ef/3vpnQURE2k/D3yIOKH6HiZLKS/z25//E58+P5bc//ydKKi+RsNNk79BERKSbcqikMikpCcMwrF+xsbE29QcPHmTMmDF4eXlhGAbh4eH2CVSaNXPmTJvPLykpyd4hdUulFd9x6OsaVk0NZvL9A7m1ryuT7x/IyqlBfH6ihtKK7+wdooiIdEMOOc41depUhg0bRlBQkLWsurqayMhIamtrmTVrFl5eXvj5+V2XeAzDICwsjOzs7Otyva5w8eJF3nnnHb744gvy8/M5duwYZrOZ0tLSq76Px48f5/nnn2fPnj384x//4O677+Zf//Vf+eUvf4mTk+3/eaKjowkICODw4cOkp6d38R3dvE5W1wLwoL+HTflIf09rveZXiohIezlkUhkVFcWcOXNsyg4dOsTZs2dZvXo1y5Yts09g3Vh5eTlLliwBwNfXFw8PDyorK696zN/+9jceeughLl68yBNPPMHtt9/O9u3b+T//5//w5Zdf8t5779m0j46OJjo6mqSkJCWVHTCoX08ADpWeZ/L9A63ln5dW2tSLiIi0h0MNf1/N6dOnAfDx8bFzJN2Tt7c3u3btoqKiApPJxIMPPtjqMb/4xS+orq4mLS2NP/zhD7zyyit88cUXjB07lvXr17Nnz57rELnj8ffqzYM/7cuK9EK2fXma8ppLbPvyNL9JP8LIO/qql1JERK6Jkkoah59nz54NQFxcnHXO3pXD0eXl5Tz33HMEBATQq1cvvL29iYmJ4ciRI03Ot2fPHubOncuQIUNwc3PDzc2NESNGNOl5y87OxjAMAHJycpqdL5iQkNAkFgvLHNEr5xaaTCYMw2DOnDkcPXqU6OhovL29MQwDk8lkbZeens7YsWPx8PDA1dWVoKAg1qxZQ319/TW9h25ubowbNw5PT882tT927Bh79+4lIiKCSZMmWct79OjBSy+9BMD69euvKRZpXeIEPwK8XFn4cQEjX9rNwo8LCPByJeExP3uHJiIi3ZRDDn//WHx8vHWenmW+JWCdC1hSUkJ4eDinTp1i/PjxREVFUV5eTmpqKjt37mT37t2EhIRYz/fKK69QXFzMqFGjmDZtGlVVVezYsYNnnnmGoqIiXn/9dev54+PjSUxMxNfX12ZI3hLDtbJcPzAwkNmzZ1NZWUnPno3DmsuXL2f16tUMGjSImJgY3N3d2bt3L0uXLuXgwYNs2bKlQ9duC0uSPH78+CZ1I0eOpH///uTk5HR5HI6qf28X1kYFaJ9KERHpNEoqaewNtMzTa26+5VNPPcWZM2fYuXMn48aNs5a/8MILjBgxgvnz5/Pll19ay9955x38/f1tzlFXV8ekSZNYt24dv/rVr7jjjjvw8/MjISGBxMRE6587y/79+1mxYgUrV660Kf/zn//M6tWrmThxIikpKdxyyy0AmM1mfvnLX/K73/2O1NRUYmJiOi2W5hw/fhyAu+++u0mdYRgEBATw//7f/+PixYvWGKXz+Xv1VjIpIiKdQsPfrSgoKOCzzz5j9uzZNgklwODBg5k/fz6FhYU2w+A/TigBXFxcWLBgAfX19ddlrqCPjw8vvPBCk/K3334bgHfffdcmWTMMg5dffhnDMEhOTu7y+KqrqwHo169fs/Xu7u427UREROTGpp7KVhw4cACAM2fONNuTePToUet3yxZFNTU1rFmzhrS0NEpKSrhw4YLNMZZFQV1p6NCh1uHuKx04cIA+ffrw/vvvN3tc7969rfckIiIi0lZKKlth2RYnMzOTzMzMFttZEsfa2lrCw8PJz89n+PDh1j0vXVxcMJlMbNy4ke+//77L4x4wYECz5ZWVldTV1ZGYmNjisT9OgruCpYeypZ7Iv//978APPZbSeVzOF+NcfYL6fndQ5xFg73BEROQmoaSyFZak5q233mLhwoWttk9PTyc/P5958+Y1Wb38ySefsHHjxnZd37IBeF1dXZO6qw0NW1aV/5i7uzuGYXDu3Ll2xdHZLHMpLXMrr2Q2mykuLua2226jT58+1zu0m47Td43/MTIuVdF/XwK9vjlkrft+4IOcH7eOBlePlg4XERFpE82pbIVlVXdeXl6b2peUlAAwZcqUJnW5ubnNHuPk5NTiVj4eHo2/7E+dOtWkrqCgoE0xXSkkJISKiopmk7nryfIIzF27djWp+/zzz6mqqiIsLOw6R9V9GZcvtvjl89HD+Hz0MANSIul1/jg8ngS/PgaPJ9Hr/HE8/rK4xWNFRETaSj2VrRg5ciQhISEkJyczZcoUZsyYYVPf0NBAbm6uNQHy9fUFYN++fURGRlrb5eTktLjvoqenJydPnmy2bsSIEQB8+OGHzJo1y9pzmZeXx6ZNm9p9P88++yzbt29n7ty5pKWl4eXlZVN/5swZzp8/z7333tvuc7fH4MGDCQ0NZc+ePXz66afWvSovX75sXWA0f/78Lo3hZjJwwwOtNzI3QORaCJzW+DpwGpjN9EqJa/H40//6v50XpIiI3NSUVLZBcnIyERERxMbGsnbtWh544AFcXV05ceIEeXl5nD17lkuXLgEQGRmJn58fr776KkeOHCEoKIiioiK2bdtGVFQUqampTc4/ZswYNm/ezPTp0xk+fDjOzs5MnjyZ4OBgRo0axejRo8nKymL06NGEhoZSVlZGRkYGkZGRbN26tV33MmHCBFasWMGqVasICAhgwoQJ+Pr6UlFRQXFxMbm5ubz44ovXlFQuWbLEOqxeWFhoLXNzcwNg2bJl3HPPPdb277zzDg899BDTpk3jiSee4LbbbmPHjh18+eWXzJs3j4iIiHbHIK244yHb174P2ycOERG56SipbAN/f38KCgp44403SEtL44MPPsDZ2ZmBAwcSGhrK9OnTrW3d3NzIyspi6dKl7N27l+zsbAIDA9m0aRMDBgxoNqlct24dAFlZWWzdupWGhgZ8fHwIDg7GMAwyMjJYvHgxmZmZFBYWMnToUDIyMjh9+nS7k0qAlStXEhoayptvvsnu3bupqqrCy8sLf39/EhISePLJJ6/pfUpJSaGsrMym7Mr7nTNnjk1Sed999/H555/z/PPPs337dv7xj38QEBDAm2++yb/9279dUwyO6pu4L1qss+mFPPHZDz2VAGX7ATg7bQt1/e/sqvBERMQBGGaz2WzvIK6XpKQk4uLi2LBhQ5MNzqX7uNbP8dixY/Tq1YsePXp0XXA3IMtCHY+/LKJn9VcYk9Y09lCW7cf86RJqPYdQMan5LaZEREQuX75snd53NQ65UMfyfO/Y2Fh7hyLtMHPmTAzDIC4uzt6hdCsNvT1p6O3J+XHrqPUcAilx8PpgSImj1nMI58essXeIIiJyE3Co4e9hw4YRHx9vfW3ZrFy6h+joaAICfthXsaPPR3c0Da4eVEx6X/tUiohIl3Co4W9pO5PJRFJSUqvt+vfvz6JFi7o8ns7gqMPfIiIiHdHW4W8lldKs7OzsNq2+9vX1xWQydX1AnUBJpYiISPu1Nal0qOFvabvw8HD0/w0RERFpK4dcqCMiIiIinUtJpYiIiIh0mJJKEREREekwJZUiIiIi0mFaqCMOpa6uzt4hiIiIdCtt/d2ppFIcRs+ePamtraW+vt7eoYiIiNx0tE+lOIz6+noaGhrsHYaIiEi305Y9npVUioiIiEiHaaGOiIiIiHSYkkoRERER6TAllSIiIiLSYUoqRURERKTDlFSKiIiISIcpqRQRERGRDtPm5yIO7Pi3NZgqLuLndQt3D+hr73BERKQbU1Ip4oAqL9TybHIB+4rPWcseCfDmzZ8Nx7NPTztGJiIi3ZU2PxdxQDN/f5D//ebvrJwaxIP+HhwqPc9v0o9w323ufPR0iL3DExGRbsih5lQmJSVhGIb1KzY21qb+4MGDjBkzBi8vLwzDIDw83D6BSrNmzpxp8/klJSXZO6Ru6fi3NewrPsfKqUFMvn8gt/Z1ZfL9A0mcGkju8XMc/7bG3iGKiEg35JDD31OnTmXYsGEEBQVZy6qrq4mMjKS2tpZZs2bh5eWFn5/fdYnHMAzCwsLIzs6+LtfrChcvXuSdd97hiy++ID8/n2PHjmE2myktLW3xffzTn/7Erl27yM/P5/Dhw1y8eJH4+HgSEhKabR8dHU1AQACHDx8mPT29627mJmequAjAg/4eNuUj/T2t9ZpfKSIi7eWQSWVUVBRz5syxKTt06BBnz55l9erVLFu2zD6BdWPl5eUsWbIEAF9fXzw8PKisrLzqMa+//jo5OTm4u7tz2223UVxcfNX20dHRREdHk5SUpKSyA/y8bgHgUOl5Jt8/0Fr+eWmlTb2IiEh7ONTw99WcPn0aAB8fHztH0j15e3uza9cuKioqMJlMPPjgg60es2rVKo4dO0ZVVRWrVq26DlEKwN0D+vJIgDe/ST/Cti9PU15ziW1fniY+/a88ere3eilFROSaKKmkcfh59uzZAMTFxVnn7F05HF1eXs5zzz1HQEAAvXr1wtvbm5iYGI4cOdLkfHv27GHu3LkMGTIENzc33NzcGDFiBO+9955Nu+zsbAzDACAnJ6fZ+YIJCQlNYrGwzBG9cm6hyWTCMAzmzJnD0aNHiY6OxtvbG8MwMJlM1nbp6emMHTsWDw8PXF1dCQoKYs2aNdTX11/Te+jm5sa4cePw9PRs8zGPPvood999t/U9kOvnzZ8N577b3Fn4cQEjX9rNwo8LuO82d9bFDrd3aCIi0k055PD3j8XHx1vn6VnmWwLWuYAlJSWEh4dz6tQpxo8fT1RUFOXl5aSmprJz5052795NSMgPK2ZfeeUViouLGTVqFNOmTaOqqoodO3bwzDPPUFRUxOuvv249f3x8PImJifj6+toMyVtiuFaW6wcGBjJ79mwqKyvp2bNxq5jly5ezevVqBg0aRExMDO7u7uzdu5elS5dy8OBBtmzZ0qFry43Ps09PPno6RPtUiohI5zE7kA0bNpgB84YNG9pV99BDD5ldXFzMu3btsikvKioy9+3b1xwcHGxT/tVXXzU5x+XLl83jxo0zOzs7m8vKymzqAHNYWFizMcfHx5sB8549e9oUc2lpqRkwA+YVK1Y0OWbXrl1mwDxx4kTzhQsXrOUNDQ3mBQsWmAFzSkpKs7G0x2OPPWYGzKWlpW1qn5ycbAbM8fHxrba92mclIiIi9qHh71YUFBTw2WefMXv2bMaNG2dTN3jwYObPn09hYaHNMLi/v3+T87i4uLBgwQLq6+vZs2dPl8ft4+PDCy+80KT87bffBuDdd9/lllt+WJBhGAYvv/wyhmGQnJzc5fGJiIjIzUXD3604cOAAAGfOnGl2q5ujR49av1u2KKqpqWHNmjWkpaVRUlLChQsXbI6xLArqSkOHDrUOd1/pwIED9OnTh/fff7/Z43r37m29JxEREZG2UlLZCsu2OJmZmWRmZrbYzpI41tbWEh4eTn5+PsOHD7fueeni4oLJZGLjxo18//33XR73gAEDmi2vrKykrq6OxMTEFo/9cRIsDqr8KFR+BZ53wq332DsaERG5wSmpbIW7uzsAb731FgsXLmy1fXp6Ovn5+cybN4/169fb1H3yySds3LixXdd3cmqcoVBXV9ekrrq6usXjWlpR7e7ujmEYnDt3rtl6cXAXzsHFSti2CMr2/1Du+zD8y1r4yWB7RSYiIjc4zalshWVVd15eXpval5SUADBlypQmdbm5uc0e4+Tk1OJWPh4ejU89OXXqVJO6goKCNsV0pZCQECoqKjh+/Hi7j5WbSO2F5r9euwv+OwS+/Ss8ngS/Ptb4/du/Npa3dFyterdFRBydeipbMXLkSEJCQkhOTmbKlCnMmDHDpr6hoYHc3FzCwsKAxqfJAOzbt4/IyEhru5ycnCY9lxaenp6cPHmy2boRI0YA8OGHHzJr1ixrz2VeXh6bNm1q9/08++yzbN++nblz55KWloaXl5dN/ZkzZzh//jz33ntvu88t3ch/3tZynbkBItdC4LTG14HTwGyGlLirH5fQcs+5iIjc/JRUtkFycjIRERHExsaydu1aHnjgAVxdXTlx4gR5eXmcPXuWS5cuARAZGYmfnx+vvvoqR44cISgoiKKiIrZt20ZUVBSpqalNzj9mzBg2b97M9OnTGT58OM7OzkyePJng4GBGjRrF6NGjycrKYvTo0YSGhlJWVkZGRgaRkZFs3bq1XfcyYcIEVqxYwapVqwgICGDChAn4+vpSUVFBcXExubm5vPjii9eUVC5ZssQ6rF5YWGgtc3NzA2DZsmXcc88Pc/PS0tJIS0sDoLS01Fpm2aT9kUceYd68ee2OQzrBHQ/ZvvZ92D5xiIhIt6Gksg38/f0pKCjgjTfeIC0tjQ8++ABnZ2cGDhxIaGgo06dPt7Z1c3MjKyuLpUuXsnfvXrKzswkMDGTTpk0MGDCg2aRy3bp1AGRlZbF161YaGhrw8fEhODgYwzDIyMhg8eLFZGZmUlhYyNChQ8nIyOD06dPtTioBVq5cSWhoKG+++Sa7d++mqqoKLy8v/P39SUhI4Mknn7ym9yklJYWysjKbsivvd86cOTZJ5eHDh5vMMf2f//kf/ud//sf6WkllF1newg4Elp7IE5/90FMJP8yvnJ+teZUiItIsw2w2m+0dxPWSlJREXFwcGzZssHl6jXQv+hy70IVzsPkpOFsEk15r7KEs2w+fLoWf3ANxLe+AICIijs0hF+pYnu8dGxtr71CkHWbOnIlhGMTFxdk7lJtXH2944iPwCW6cQ/n64MbvPsHwxIf2jk5ERG5gDjX8PWzYMOLj462vLZuVS/cQHR1NQECA9XVHn48uLejjBU+laZ9KERFpF4ca/pa2M5lMJCUltdquf//+LFq0qMvjERERkRubkkppVnZ2NhEREa228/X1ta7WFhEREcelpFJEREREOswhF+qIiIiISOdSUikiIiIiHaakUkREREQ6TEmliIiIiHSYQ+1TKY6tvr6ehoYGe4chIiLS7fTo0aPVNkoqxWF8/fXX1NbW2jsMERGRbmfw4MGttlFSKQ6jtrYWZ2dnXFz0Yy8iItJWdXV1bWqn367iUFxcXNrUhS8iIiLto4U6IiIiItJhSipFREREpMOUVIqIiIhIhympFBEREZEOU1IpIiIiIh2mpFJEREREOkxbCok4sNKK7zhZXcugfj3x9+pt73BERKQbU1Ip4oCqvqsjfoeJQ1/XWMse/GlfEif40b+3/lkQEZH20/C3iAP57nI9312uZ8X2UkoqL/Hbn/8Tnz8/lt/+/J8oqbxEwk6TvUMUEZFuyqGSyqSkJAzDsH7Fxsba1B88eJAxY8bg5eWFYRiEh4fbJ1Bp1syZM20+v6SkJHuH1O2MfedLxr7zJV+c/AerpgYz+f6B3NrXlcn3D2Tl1CA+P1FDacV39g5TRES6IYcc55o6dSrDhg0jKCjIWlZdXU1kZCS1tbXMmjULLy8v/Pz8rks8hmEQFhZGdnb2dbleV7h48SLvvPMOX3zxBfn5+Rw7dgyz2UxpaWmz72NFRQWpqals27aNI0eOcOrUKfr27cuDDz7IokWLeOyxx5ocEx0dTUBAAIcPHyY9Pf063NXN7UF/D5vXI/09AThZXav5lSIi0m4OmVRGRUUxZ84cm7JDhw5x9uxZVq9ezbJly+wTWDdWXl7OkiVLAPD19cXDw4PKysoW22/ZsoVf/OIX3H777YwZM4bbb7+dkydPkpqayo4dO3jttdes57OIjo4mOjqapKQkJZXXaPcv7sdUeYmn/79jHCo9z+T7B1rrPi9t/LwG9etpr/BERKQbc6jh76s5ffo0AD4+PnaOpHvy9vZm165dVFRUYDKZePDBB6/afvDgwWzbto0TJ07w4Ycfsnr1aj766CMKCgro168fy5cvt34m0nl693Dm3gF9ePCnfVmRXsi2L09TXnOJbV+e5jfpRxh5R1/1UoqIyDVRUknj8PPs2bMBiIuLs87Zu3I4ury8nOeee46AgAB69eqFt7c3MTExHDlypMn59uzZw9y5cxkyZAhubm64ubkxYsQI3nvvPZt22dnZGIYBQE5OTrPzBRMSEprEYmGZI3rl3EKTyYRhGMyZM4ejR48SHR2Nt7c3hmFgMpms7dLT0xk7diweHh64uroSFBTEmjVrqK+vv6b30M3NjXHjxuHp6dmm9mPGjGHy5Mk4Odn+CA4ZMoQZM2Zw+fJlPvvss2uKRVqXOMGPAC9XFn5cwMiXdrPw4wICvFxJeMzP3qGJiEg35ZDD3z8WHx9vnadnmW8JWOcClpSUEB4ezqlTpxg/fjxRUVGUl5eTmprKzp072b17NyEhIdbzvfLKKxQXFzNq1CimTZtGVVUVO3bs4JlnnqGoqIjXX3/dev74+HgSExPx9fW1GZK3xHCtLNcPDAxk9uzZVFZW0rNn47Dm8uXLWb16NYMGDSImJgZ3d3f27t3L0qVLOXjwIFu2bOnQtTuqR48eALi46Mezq/Tv7cLaqADtUykiIp1Gv7Vp7A20zNNrbr7lU089xZkzZ9i5cyfjxo2zlr/wwguMGDGC+fPn8+WXX1rL33nnHfz9/W3OUVdXx6RJk1i3bh2/+tWvuOOOO/Dz8yMhIYHExETrnzvL/v37WbFiBStXrrQp//Of/8zq1auZOHEiKSkp3HLLLQCYzWZ++ctf8rvf/Y7U1FRiYmI6LZb2qKmpISUlBVdXVx599FG7xOBI/L16K5kUEZFOoeHvVhQUFPDZZ58xe/Zsm4QSGucFzp8/n8LCQpth8B8nlNDY67ZgwQLq6+vZs2dPl8ft4+PDCy+80KT87bffBuDdd9+1JpTQOAXg5ZdfxjAMkpOTuzy+lixYsIBvv/2W5cuX4+XlZbc4REREpH3UU9mKAwcOAHDmzJlmexKPHj1q/W7ZoqimpoY1a9aQlpZGSUkJFy5csDnmeixAGTp0qHW4+0oHDhygT58+vP/++80e17t3b+s9XW/Lly/n448/ZsKECSxfvtwuMYiIiMi1UVLZCsu2OJmZmWRmZrbYzpI41tbWEh4eTn5+PsOHD7fueeni4oLJZGLjxo18//33XR73gAEDmi2vrKykrq6OxMTEFo/9cRJ8PSQmJrJ69WrGjBnDH//4R5ydna97DDc7l/PFOFefoL7fHdR5BNg7HBERuckoqWyFu7s7AG+99RYLFy5stX16ejr5+fnMmzeP9evX29R98sknbNy4sV3Xt6yOrqura1JXXV3d4nGWVeU/5u7ujmEYnDt3rl1xdKXExEQSEhIIDw/nT3/6E717a45fZ3H6rhLjUhX99yXQ65tD1vLvBz5I1SMJ1HvcacfoRETkZqI5la2wrOrOy8trU/uSkhIApkyZ0qQuNze32WOcnJxa3MrHw6PxqSenTp1qUldQUNCmmK4UEhJCRUUFx48fb/exXSEhIYGEhATCwsLIzMy0mecpbWNcvtjil89HDzMgJZJe54/D40nw62PweBK9zh9nQErkVY8VERFpD/VUtmLkyJGEhISQnJzMlClTmDFjhk19Q0MDubm5hIWFAY1PkwHYt28fkZGR1nY5OTlNei4tPD09OXnyZLN1I0aMAODDDz9k1qxZ1p7LvLw8Nm3a1O77efbZZ9m+fTtz584lLS2tyWKYM2fOcP78ee699952n7u94uPjWblyJY8++qgSyg4YuOGBqzcwN0DkWgic1vg6cBqYzZASd9VjT//r/3ZekCIictNTUtkGycnJREREEBsby9q1a3nggQdwdXXlxIkT5OXlcfbsWS5dugRAZGQkfn5+vPrqqxw5coSgoCCKiorYtm0bUVFRpKamNjn/mDFj2Lx5M9OnT2f48OE4OzszefJkgoODGTVqFKNHjyYrK4vRo0cTGhpKWVkZGRkZREZGsnXr1nbdy4QJE1ixYgWrVq0iICCACRMm4OvrS0VFBcXFxeTm5vLiiy9eU1K5ZMkS67B6YWGhtczNzQ2AZcuWcc899wCNG7evXLkSFxcXRo4cyWuvvdbkfOHh4YSHh7c7DmnGHQ/ZvvZ92D5xiIjITUtJZRv4+/tTUFDAG2+8QVpaGh988AHOzs4MHDiQ0NBQpk+fbm3r5uZGVlYWS5cuZe/evWRnZxMYGMimTZsYMGBAs0nlunXrAMjKymLr1q00NDTg4+NDcHAwhmGQkZHB4sWLyczMpLCwkKFDh5KRkcHp06fbnVQCrFy5ktDQUN588012795NVVUVXl5e+Pv7k5CQwJNPPnlN71NKSgplZWU2ZVfe75w5c6xJpeXpPnV1ddbN4JujpLJ138R90WKdtSfyxGc/9FQClO0H4Oy0LdT117xKERHpOMNsNpvtHcT1kpSURFxcHBs2bGiywbl0H9f6OR47doxevXpZn9jjCJy+q8TjL4voWf0VxqQ1jT2UZfsxf7qE2n53URHZvoVjIiLieC5fvmyd3nc1DrlQx/J879jYWHuHIu0wc+ZMDMMgLi7O3qF0Gw29PTk/bh21nkMgJQ5eHwwpcdR6DuH8uLX2Dk9ERG4iDjX8PWzYMOLj462vLZuVS/cQHR1NQMAP+yt29PnojqLB1YOKSe9rn0oREelSDjX8LW1nMplISkpqtV3//v1ZtGhRl8fTGRxx+FtERKSj2jr8raRSmpWdnU1ERESr7Xx9fa2Lbm50SipFRETar61JpUMNf0vbhYeHo/9viIiISFs55EIdEREREelcSipFREREpMOUVIqIiIhIhympFBEREZEOU1IpIiIiIh2m1d/iUOrq6uwdgoiISLfS1t+dSirFYfTs2ZPa2lrq6+vtHYqIiMhNR5ufi8Oor6+noaHB3mGIiIh0O215cIiSShERERHpMC3UEREREZEOU1IpIiIiIh2mpFJEREREOkxJpYiIiIh0mJJKEREREekwJZUiIiIi0mHa/FzEgR3/tgZTxUX8vG7h7gF97R2OiIh0Y0oqRRxQ5YVank0uYF/xOWvZIwHevPmz4Xj26WnHyEREpLvS5uciDmjm7w/yv9/8nZVTg3jQ34NDpef5TfoR7rvNnY+eDrF3eCIi0g051JzKpKQkDMOwfsXGxtrUHzx4kDFjxuDl5YVhGISHh9snUGnWzJkzbT6/pKQke4fULR3/toZ9xedYOTWIyfcP5Na+rky+fyCJUwPJPX6O49/W2DtEERHphhxy+Hvq1KkMGzaMoKAga1l1dTWRkZHU1tYya9YsvLy88PPzuy7xGIZBWFgY2dnZ1+V6XeHixYu88847fPHFF+Tn53Ps2DHMZjOlpaXNvo9ms5n/+3//L4cOHeLYsWNUVlbSr18/7rrrLp5++mmeeuqpJs8ZjY6OJiAggMOHD5Oenn6d7uzmY6q4CMCD/h425SP9Pa31ml8pIiLt5ZBJZVRUFHPmzLEpO3ToEGfPnmX16tUsW7bMPoF1Y+Xl5SxZsgQAX19fPDw8qKysbLF9fX09b731FiNGjGDy5Mn85Cc/4fz58+zYsYN58+axZcsWPv30U5ycfuhMj46OJjo6mqSkJCWVHeDndQsAh0rPM/n+gdbyz0srbepFRETawyGTyuacPn0aAB8fHztH0j15e3uza9cuHnjgATw9PZkwYQI7d+5ssb2LiwtVVVW4urralNfV1TF+/Hh27tzJ9u3bmTx5cleH7nDuHtCXRwK8+U36EcyYGenvyeellcSn/5VH7/ZWL6WIiFwTh5pT2RLDMJg9ezYAcXFx1jl7Vw5Hl5eX89xzzxEQEECvXr3w9vYmJiaGI0eONDnfnj17mDt3LkOGDMHNzQ03NzdGjBjBe++9Z9MuOzsbwzAAyMnJaXa+YEJCQpNYLCxzRK+cW2gymTAMgzlz5nD06FGio6Px9vbGMAxMJpO1XXp6OmPHjsXDwwNXV1eCgoJYs2YN9fX11/Qeurm5MW7cODw9Pdt8zI8TSmhMNqOiogAoLi6+plikdW/+bDj33ebOwo8LGPnSbhZ+XMB9t7mzLna4vUMTEZFuSj2VQHx8vHWenmW+JWCdC1hSUkJ4eDinTp1i/PjxREVFUV5eTmpqKjt37mT37t2EhPywYvaVV16huLiYUaNGMW3aNKqqqtixYwfPPPMMRUVFvP7669bzx8fHk5iYiK+vr82QvCWGa2W5fmBgILNnz6ayspKePRu3ilm+fDmrV69m0KBBxMTE4O7uzt69e1m6dCkHDx5ky5YtHbp2RzQ0NLBjxw4Amzmv0rk8+/Tko6dDtE+liIh0HrMD2bBhgxkwb9iwoV11Dz30kNnFxcW8a9cum/KioiJz3759zcHBwTblX331VZNzXL582Txu3Dizs7OzuayszKYOMIeFhTUbc3x8vBkw79mzp00xl5aWmgEzYF6xYkWTY3bt2mUGzBMnTjRfuHDBWt7Q0GBesGCBGTCnpKQ0G0t7PPbYY2bAXFpa2mrb+Ph4c3x8vPnf/u3fzPfcc48ZMM+ZM6fF9lf7rERERMQ+NPzdioKCAj777DNmz57NuHHjbOoGDx7M/PnzKSwstBkG9/f3b3IeFxcXFixYQH19PXv27OnyuH18fHjhhRealL/99tsAvPvuu9xyyw8LMgzD4OWXX8YwDJKTk7s8vislJiaSmJjIb3/7W4qKiliyZAnr16+/rjGIiIhIx2j4uxUHDhwA4MyZMyQkJDSpP3r0qPW7Zbi2pqaGNWvWkJaWRklJCRcuXLA5xrIoqCsNHTrUOtx9pQMHDtCnTx/ef//9Zo/r3bu39Z6uF7PZTENDA6dPn2bbtm38x3/8B3l5eXz66ae4u7tf11hERETk2iipbIVlW5zMzEwyMzNbbGdJHGtrawkPDyc/P5/hw4db97x0cXHBZDKxceNGvv/++y6Pe8CAAc2WV1ZWUldXR2JiYovH/jgJvh6cnJwYNGgQCxYswMvLiyeeeIKXXnqJV1555brHclMrPwqVX4HnnXDrPfaORkREbiJKKlth6Sl76623WLhwYavt09PTyc/PZ968eU2GcD/55BM2btzYrutb9mmsq6trUlddXd3icZZV5T/m7u6OYRicO3eu2fobwfjx4wG69WbwN5QL5+BiJWxbBGX7fyj3fRie+Aj6eNktNBERuXloTmUrLKu68/Ly2tS+pKQEgClTpjSpy83NbfYYJyenFrfy8fBofOrJqVOnmtQVFBS0KaYrhYSEUFFRwfHjx9t97PVimR7g4qL/87Sq9kLrX6/dBf8dAt/+FR5Pgl8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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -1300,56 +1290,17 @@
     }
    ],
    "source": [
-    "az.plot_forest(\n",
-    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_horseshoe.idata,\n",
     "    var_names=[\"beta_z\"],\n",
-    "    combined=True,\n",
-    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f09b24bf",
-   "metadata": {},
-   "source": [
-    "## The Treatment Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "acafc928",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([], dtype=object)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_forest(\n",
-    "    [result_spike_slab.idata, result_normal.idata],\n",
-    "    var_names=[\"beta_t\"],\n",
-    "    combined=True,\n",
-    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
-    ")"
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
    ]
   }
  ],

From 4577106e79a0652c0d41b8d7ffed86e15e9775f1 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 09:34:47 +0000
Subject: [PATCH 09/24] add normal test vs_prior

Signed-off-by: Nathaniel 
---
 causalpy/tests/test_variable_selection_priors.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py
index 09de5885..6aad7edb 100644
--- a/causalpy/tests/test_variable_selection_priors.py
+++ b/causalpy/tests/test_variable_selection_priors.py
@@ -100,6 +100,17 @@ def test_create_prior_horseshoe(coords, sample_data):
         assert beta.name == "beta"
 
 
+def test_create_prior_normal(coords, sample_data):
+    """Test create_prior for horseshoe."""
+    vs_prior = VariableSelectionPrior("normal")
+
+    with pm.Model(coords=coords) as model:
+        beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
+
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+
 def test_convenience_function_with_custom_hyperparams(coords):
     """Test convenience function with custom hyperparameters."""
     with pm.Model(coords=coords) as model:

From fd1bfb7c3853ca11789c73244f69f7fa0876eb25 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 20:31:25 +0000
Subject: [PATCH 10/24] improving test coverage

Signed-off-by: Nathaniel 
---
 causalpy/tests/test_integration_pymc_examples.py | 8 ++++++++
 causalpy/tests/test_variable_selection_priors.py | 2 +-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py
index efeb6bf9..233c9cd4 100644
--- a/causalpy/tests/test_integration_pymc_examples.py
+++ b/causalpy/tests/test_integration_pymc_examples.py
@@ -710,6 +710,10 @@ def test_iv_reg_vs_prior(mock_pymc_sample):
         result.idata, "beta_z"
     )
     assert isinstance(summary, pd.DataFrame)
+    with pytest.raises(ValueError):
+        summary = result.model.vs_prior_outcome.get_shrinkage_factors(
+            result.idata, "beta_z"
+        )
 
 
 @pytest.mark.integration
@@ -745,6 +749,10 @@ def test_iv_reg_vs_prior_hs(mock_pymc_sample):
         result.idata, "beta_z"
     )
     assert isinstance(summary, pd.DataFrame)
+    with pytest.raises(ValueError):
+        summary = result.model.vs_prior_outcome.get_inclusion_probabilities(
+            result.idata, "beta_z"
+        )
 
 
 @pytest.mark.integration
diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py
index 6aad7edb..1b464be6 100644
--- a/causalpy/tests/test_variable_selection_priors.py
+++ b/causalpy/tests/test_variable_selection_priors.py
@@ -78,7 +78,7 @@ def test_create_variable_in_model_context_horseshoe(coords):
 
 def test_create_prior_spike_and_slab(coords):
     """Test create_prior for spike-and-slab."""
-    vs_prior = VariableSelectionPrior("spike_and_slab")
+    vs_prior = VariableSelectionPrior("spike_and_slab", hyperparams={"pi_alpha": 5})
 
     with pm.Model(coords=coords) as model:
         beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")

From 7375aa584e1050f1df7b7c01222833744b0a26a7 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 21:20:28 +0000
Subject: [PATCH 11/24] updating notebook

Signed-off-by: Nathaniel 
---
 causalpy/pymc_models.py                  |    8 +-
 docs/source/notebooks/iv_vs_priors.ipynb | 1461 ++++++++++++++++------
 2 files changed, 1070 insertions(+), 399 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index 7d30dd5a..e04f446f 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -923,6 +923,7 @@ def fit_outcome_model(
         normal_outcome: bool = True,
         spline_component: bool = False,
         winsorize_boundary: float = 0.0,
+        spline_knots: int = 30,
     ) -> tuple[az.InferenceData, pm.Model]:
         """
         Fit a Bayesian outcome model using covariates and previously estimated propensity scores.
@@ -963,6 +964,9 @@ def fit_outcome_model(
             If we wish to winsorize the propensity score this can be set to clip the high
             and low values of the propensity at 0 + winsorize_boundary and 1-winsorize_boundary
 
+        spline_knots: int, default 30
+            The number of knots we use in the 0 - 1 interval to create our spline function
+
         Returns
         -------
         idata_outcome : arviz.InferenceData
@@ -1026,11 +1030,11 @@ class initialisation.
                     "beta_ps_spline",
                     priors["beta_ps"][0],
                     priors["beta_ps"][1],
-                    size=34,
+                    size=spline_knots + 4,
                 )
                 B = dmatrix(
                     "bs(ps, knots=knots, degree=3, include_intercept=True, lower_bound=0, upper_bound=1) - 1",
-                    {"ps": p, "knots": np.linspace(0, 1, 30)},
+                    {"ps": p, "knots": np.linspace(0, 1, spline_knots)},
                 )
                 B_f = np.asarray(B, order="F")
                 splines_summed = pm.Deterministic(
diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 97e221ad..bd12eed6 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2,16 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 1,
    "id": "532c6736",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     }
    ],
@@ -28,6 +27,18 @@
     "%autoreload 2"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b1b3aa75",
+   "metadata": {},
+   "source": [
+    "## Variable Selection Priors and Instrumental Variable Designs\n",
+    "\n",
+    "When building causal inference models, we often face a dilemma: we want to control for confounders to get unbiased causal estimates, but we're not always certain which variables are the true confounders. Include too few, and we risk omitted variable bias. Include too many, and we introduce noise that inflates our uncertainty or, worse, creates multicollinearity that destabilizes our estimates.\n",
+    "\n",
+    "Traditional approaches force us to make hard choices upfront—which variables to include, which to exclude. This in ideal cases should be driven by theory. But what if we could let the data help us make these decisions while still maintaining the principled probabilistic framework of Bayesian inference? This is where variable selection priors come in. Let's first simulate some data with some natural confounding structure. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -81,123 +92,123 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      17.285021\n",
-       "      5.560022\n",
-       "      4.908333\n",
-       "      1\n",
-       "      4.221970\n",
-       "      -0.711212\n",
-       "      0.678770\n",
-       "      0.807216\n",
-       "      1.502488\n",
-       "      -0.194990\n",
+       "      3.169837\n",
+       "      -0.170346\n",
+       "      1.113394\n",
+       "      0\n",
+       "      9.236102\n",
+       "      1.294441\n",
+       "      0.418241\n",
+       "      0.536286\n",
+       "      -0.615573\n",
+       "      -1.173784\n",
        "      ...\n",
-       "      -0.355420\n",
-       "      1.402186\n",
-       "      -1.517787\n",
-       "      0.292296\n",
-       "      -0.439312\n",
-       "      -0.400863\n",
-       "      1.478663\n",
-       "      1.030321\n",
-       "      1.535974\n",
-       "      1.001401\n",
+       "      0.559393\n",
+       "      1.111766\n",
+       "      -0.216069\n",
+       "      0.451496\n",
+       "      -0.863189\n",
+       "      0.319180\n",
+       "      0.268475\n",
+       "      -0.438362\n",
+       "      0.347809\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      -5.418737\n",
-       "      -1.169369\n",
-       "      -1.416456\n",
-       "      0\n",
-       "      3.571828\n",
-       "      -0.971269\n",
-       "      0.155694\n",
-       "      0.094208\n",
-       "      1.060349\n",
-       "      0.918542\n",
+       "      10.479049\n",
+       "      6.662990\n",
+       "      2.272020\n",
+       "      1\n",
+       "      3.787487\n",
+       "      -0.885005\n",
+       "      0.315013\n",
+       "      0.810138\n",
+       "      1.137214\n",
+       "      0.203685\n",
        "      ...\n",
-       "      0.647043\n",
-       "      0.978512\n",
-       "      0.102692\n",
-       "      -1.338446\n",
-       "      1.164631\n",
-       "      1.157542\n",
-       "      -0.477164\n",
-       "      -0.677874\n",
-       "      -0.459080\n",
-       "      -0.998202\n",
+       "      -0.017179\n",
+       "      0.410818\n",
+       "      0.670074\n",
+       "      1.954944\n",
+       "      0.888255\n",
+       "      -0.506518\n",
+       "      0.916637\n",
+       "      1.321011\n",
+       "      0.726014\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      8.329916\n",
-       "      5.750195\n",
-       "      1.859907\n",
+       "      7.307821\n",
+       "      4.118982\n",
+       "      2.062946\n",
        "      1\n",
-       "      6.389524\n",
-       "      0.155810\n",
-       "      0.097990\n",
-       "      1.886364\n",
-       "      0.204515\n",
-       "      1.407368\n",
+       "      3.311157\n",
+       "      -1.075537\n",
+       "      1.058536\n",
+       "      1.908944\n",
+       "      1.122914\n",
+       "      0.611691\n",
        "      ...\n",
-       "      -0.789757\n",
-       "      -1.376844\n",
-       "      -1.164524\n",
-       "      -0.854488\n",
-       "      -0.563027\n",
-       "      -1.762406\n",
-       "      0.707221\n",
-       "      1.078595\n",
-       "      0.574396\n",
-       "      1.001401\n",
+       "      0.050641\n",
+       "      1.245489\n",
+       "      -1.070642\n",
+       "      -0.060250\n",
+       "      -1.857638\n",
+       "      0.806913\n",
+       "      0.635421\n",
+       "      0.666008\n",
+       "      0.657767\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      -14.109005\n",
-       "      -3.260798\n",
-       "      -3.616069\n",
-       "      0\n",
-       "      1.522008\n",
-       "      -1.791197\n",
-       "      -0.025364\n",
-       "      -1.849614\n",
-       "      -0.420513\n",
-       "      -0.813558\n",
+       "      9.781360\n",
+       "      0.649647\n",
+       "      4.043904\n",
+       "      1\n",
+       "      8.423450\n",
+       "      0.969380\n",
+       "      0.698199\n",
+       "      0.314419\n",
+       "      1.446987\n",
+       "      -2.729092\n",
        "      ...\n",
-       "      -0.967529\n",
-       "      0.604905\n",
-       "      0.136397\n",
-       "      0.087200\n",
-       "      -0.575056\n",
-       "      -0.244472\n",
-       "      -1.225791\n",
-       "      -1.208765\n",
-       "      -1.152913\n",
-       "      -0.998202\n",
+       "      -1.052429\n",
+       "      1.143079\n",
+       "      -1.757701\n",
+       "      -1.167276\n",
+       "      -0.164620\n",
+       "      1.687004\n",
+       "      0.854768\n",
+       "      -0.227239\n",
+       "      1.304402\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      15.927794\n",
-       "      6.070905\n",
-       "      4.285630\n",
+       "      5.739283\n",
+       "      6.812030\n",
+       "      0.642417\n",
        "      1\n",
-       "      8.262360\n",
-       "      0.904944\n",
-       "      -0.648125\n",
-       "      -0.420076\n",
-       "      0.774960\n",
-       "      0.612525\n",
+       "      6.613922\n",
+       "      0.245569\n",
+       "      -0.338491\n",
+       "      0.814305\n",
+       "      -0.859798\n",
+       "      0.334968\n",
        "      ...\n",
-       "      2.362694\n",
-       "      2.702110\n",
-       "      -1.565191\n",
-       "      1.689404\n",
-       "      1.198610\n",
-       "      0.117392\n",
-       "      1.361744\n",
-       "      1.160004\n",
-       "      1.339552\n",
-       "      1.001401\n",
+       "      -0.547622\n",
+       "      0.778724\n",
+       "      0.678956\n",
+       "      1.715229\n",
+       "      -0.439130\n",
+       "      -0.238847\n",
+       "      0.496327\n",
+       "      1.359385\n",
+       "      0.194070\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      ...\n",
@@ -225,123 +236,123 @@
        "    \n",
        "    \n",
        "      2495\n",
-       "      -6.973181\n",
-       "      -2.492475\n",
-       "      -0.493568\n",
-       "      1\n",
-       "      6.399022\n",
-       "      0.159609\n",
-       "      2.004918\n",
-       "      1.850936\n",
-       "      -0.205821\n",
-       "      1.826866\n",
+       "      -5.099912\n",
+       "      -0.870746\n",
+       "      -1.409722\n",
+       "      0\n",
+       "      6.565630\n",
+       "      0.226252\n",
+       "      1.589653\n",
+       "      0.056005\n",
+       "      -0.386026\n",
+       "      -0.462251\n",
        "      ...\n",
-       "      0.180511\n",
-       "      -0.656888\n",
-       "      -1.253321\n",
-       "      1.057077\n",
-       "      -0.803677\n",
-       "      -0.759486\n",
-       "      -0.611072\n",
-       "      -1.013733\n",
-       "      -0.167970\n",
-       "      1.001401\n",
+       "      0.987633\n",
+       "      0.246870\n",
+       "      -0.202917\n",
+       "      0.178579\n",
+       "      0.763186\n",
+       "      0.527462\n",
+       "      -0.464865\n",
+       "      -0.618693\n",
+       "      -0.475800\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2496\n",
-       "      -7.457602\n",
-       "      1.094568\n",
-       "      -2.850723\n",
+       "      -32.742858\n",
+       "      -7.337551\n",
+       "      -8.468435\n",
        "      0\n",
-       "      9.043466\n",
-       "      1.217386\n",
-       "      -1.788360\n",
-       "      -0.342299\n",
-       "      -0.635556\n",
-       "      0.595195\n",
+       "      4.760520\n",
+       "      -0.495792\n",
+       "      0.546002\n",
+       "      0.209072\n",
+       "      0.666614\n",
+       "      0.400847\n",
        "      ...\n",
-       "      -0.020930\n",
-       "      -0.459529\n",
-       "      1.528013\n",
-       "      0.135980\n",
-       "      -0.448317\n",
-       "      -0.113937\n",
-       "      -0.652803\n",
-       "      -0.103194\n",
-       "      -0.911497\n",
-       "      -0.998202\n",
+       "      -0.798775\n",
+       "      -0.616483\n",
+       "      0.431552\n",
+       "      1.238957\n",
+       "      0.957759\n",
+       "      -0.583051\n",
+       "      -2.916171\n",
+       "      -2.283696\n",
+       "      -2.779944\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2497\n",
-       "      -12.389506\n",
-       "      -5.563091\n",
-       "      -2.275472\n",
+       "      6.759804\n",
+       "      1.912040\n",
+       "      1.615921\n",
        "      0\n",
-       "      2.631578\n",
-       "      -1.347369\n",
-       "      0.948935\n",
-       "      0.733226\n",
-       "      0.776689\n",
-       "      1.007780\n",
+       "      10.445934\n",
+       "      1.778373\n",
+       "      0.097808\n",
+       "      -0.807658\n",
+       "      0.380358\n",
+       "      -0.455391\n",
        "      ...\n",
-       "      0.394645\n",
-       "      -1.223821\n",
-       "      -0.281657\n",
-       "      -0.934772\n",
-       "      -0.587933\n",
-       "      1.399632\n",
-       "      -1.077664\n",
-       "      -1.793181\n",
-       "      -0.730043\n",
-       "      -0.998202\n",
+       "      0.668040\n",
+       "      1.471963\n",
+       "      0.573966\n",
+       "      -0.288768\n",
+       "      -0.861025\n",
+       "      0.372657\n",
+       "      0.586824\n",
+       "      0.097788\n",
+       "      0.511847\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2498\n",
-       "      4.181542\n",
-       "      1.763852\n",
-       "      1.805896\n",
-       "      1\n",
-       "      6.074689\n",
-       "      0.029876\n",
-       "      1.265076\n",
-       "      -1.047612\n",
-       "      -0.174857\n",
-       "      -1.611537\n",
+       "      -11.249395\n",
+       "      -1.938808\n",
+       "      -3.103529\n",
+       "      0\n",
+       "      5.715321\n",
+       "      -0.113872\n",
+       "      0.747480\n",
+       "      1.635159\n",
+       "      -1.136585\n",
+       "      -0.007239\n",
        "      ...\n",
-       "      0.660882\n",
-       "      1.338977\n",
-       "      -1.475265\n",
-       "      -1.475989\n",
-       "      0.910175\n",
-       "      0.325985\n",
-       "      0.349857\n",
-       "      0.066698\n",
-       "      0.557359\n",
-       "      1.001401\n",
+       "      -2.404202\n",
+       "      -2.074570\n",
+       "      0.022878\n",
+       "      1.345018\n",
+       "      0.705361\n",
+       "      0.414329\n",
+       "      -1.010186\n",
+       "      -0.893686\n",
+       "      -1.028702\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2499\n",
-       "      3.783388\n",
-       "      4.814916\n",
-       "      0.656157\n",
+       "      21.658258\n",
+       "      7.675401\n",
+       "      5.660952\n",
        "      1\n",
-       "      3.824017\n",
-       "      -0.870393\n",
-       "      -0.000393\n",
-       "      -0.373005\n",
-       "      -1.857066\n",
-       "      0.327473\n",
+       "      5.741192\n",
+       "      -0.103523\n",
+       "      -1.083828\n",
+       "      0.896827\n",
+       "      0.146243\n",
+       "      1.363973\n",
        "      ...\n",
-       "      0.725508\n",
-       "      -1.219133\n",
-       "      -2.110389\n",
-       "      -0.375057\n",
-       "      0.280377\n",
-       "      -0.899183\n",
-       "      0.315558\n",
-       "      0.841183\n",
-       "      0.194693\n",
-       "      1.001401\n",
+       "      -1.310958\n",
+       "      -0.004224\n",
+       "      0.206620\n",
+       "      0.012787\n",
+       "      -1.777783\n",
+       "      0.340176\n",
+       "      1.907981\n",
+       "      1.581676\n",
+       "      1.832247\n",
+       "      0.977650\n",
        "    \n",
        "  \n",
        "\n",
@@ -349,57 +360,57 @@
        ""
       ],
       "text/plain": [
-       "         Y_cont     Y_bin    T_cont  T_bin     alpha  feature_0  feature_1  \\\n",
-       "0     17.285021  5.560022  4.908333      1  4.221970  -0.711212   0.678770   \n",
-       "1     -5.418737 -1.169369 -1.416456      0  3.571828  -0.971269   0.155694   \n",
-       "2      8.329916  5.750195  1.859907      1  6.389524   0.155810   0.097990   \n",
-       "3    -14.109005 -3.260798 -3.616069      0  1.522008  -1.791197  -0.025364   \n",
-       "4     15.927794  6.070905  4.285630      1  8.262360   0.904944  -0.648125   \n",
-       "...         ...       ...       ...    ...       ...        ...        ...   \n",
-       "2495  -6.973181 -2.492475 -0.493568      1  6.399022   0.159609   2.004918   \n",
-       "2496  -7.457602  1.094568 -2.850723      0  9.043466   1.217386  -1.788360   \n",
-       "2497 -12.389506 -5.563091 -2.275472      0  2.631578  -1.347369   0.948935   \n",
-       "2498   4.181542  1.763852  1.805896      1  6.074689   0.029876   1.265076   \n",
-       "2499   3.783388  4.814916  0.656157      1  3.824017  -0.870393  -0.000393   \n",
+       "         Y_cont     Y_bin    T_cont  T_bin      alpha  feature_0  feature_1  \\\n",
+       "0      3.169837 -0.170346  1.113394      0   9.236102   1.294441   0.418241   \n",
+       "1     10.479049  6.662990  2.272020      1   3.787487  -0.885005   0.315013   \n",
+       "2      7.307821  4.118982  2.062946      1   3.311157  -1.075537   1.058536   \n",
+       "3      9.781360  0.649647  4.043904      1   8.423450   0.969380   0.698199   \n",
+       "4      5.739283  6.812030  0.642417      1   6.613922   0.245569  -0.338491   \n",
+       "...         ...       ...       ...    ...        ...        ...        ...   \n",
+       "2495  -5.099912 -0.870746 -1.409722      0   6.565630   0.226252   1.589653   \n",
+       "2496 -32.742858 -7.337551 -8.468435      0   4.760520  -0.495792   0.546002   \n",
+       "2497   6.759804  1.912040  1.615921      0  10.445934   1.778373   0.097808   \n",
+       "2498 -11.249395 -1.938808 -3.103529      0   5.715321  -0.113872   0.747480   \n",
+       "2499  21.658258  7.675401  5.660952      1   5.741192  -0.103523  -1.083828   \n",
        "\n",
        "      feature_2  feature_3  feature_4  ...  feature_8  feature_9  feature_10  \\\n",
-       "0      0.807216   1.502488  -0.194990  ...  -0.355420   1.402186   -1.517787   \n",
-       "1      0.094208   1.060349   0.918542  ...   0.647043   0.978512    0.102692   \n",
-       "2      1.886364   0.204515   1.407368  ...  -0.789757  -1.376844   -1.164524   \n",
-       "3     -1.849614  -0.420513  -0.813558  ...  -0.967529   0.604905    0.136397   \n",
-       "4     -0.420076   0.774960   0.612525  ...   2.362694   2.702110   -1.565191   \n",
+       "0      0.536286  -0.615573  -1.173784  ...   0.559393   1.111766   -0.216069   \n",
+       "1      0.810138   1.137214   0.203685  ...  -0.017179   0.410818    0.670074   \n",
+       "2      1.908944   1.122914   0.611691  ...   0.050641   1.245489   -1.070642   \n",
+       "3      0.314419   1.446987  -2.729092  ...  -1.052429   1.143079   -1.757701   \n",
+       "4      0.814305  -0.859798   0.334968  ...  -0.547622   0.778724    0.678956   \n",
        "...         ...        ...        ...  ...        ...        ...         ...   \n",
-       "2495   1.850936  -0.205821   1.826866  ...   0.180511  -0.656888   -1.253321   \n",
-       "2496  -0.342299  -0.635556   0.595195  ...  -0.020930  -0.459529    1.528013   \n",
-       "2497   0.733226   0.776689   1.007780  ...   0.394645  -1.223821   -0.281657   \n",
-       "2498  -1.047612  -0.174857  -1.611537  ...   0.660882   1.338977   -1.475265   \n",
-       "2499  -0.373005  -1.857066   0.327473  ...   0.725508  -1.219133   -2.110389   \n",
+       "2495   0.056005  -0.386026  -0.462251  ...   0.987633   0.246870   -0.202917   \n",
+       "2496   0.209072   0.666614   0.400847  ...  -0.798775  -0.616483    0.431552   \n",
+       "2497  -0.807658   0.380358  -0.455391  ...   0.668040   1.471963    0.573966   \n",
+       "2498   1.635159  -1.136585  -0.007239  ...  -2.404202  -2.074570    0.022878   \n",
+       "2499   0.896827   0.146243   1.363973  ...  -1.310958  -0.004224    0.206620   \n",
        "\n",
        "      feature_11  feature_12  feature_13  Y_cont_scaled  Y_bin_scaled  \\\n",
-       "0       0.292296   -0.439312   -0.400863       1.478663      1.030321   \n",
-       "1      -1.338446    1.164631    1.157542      -0.477164     -0.677874   \n",
-       "2      -0.854488   -0.563027   -1.762406       0.707221      1.078595   \n",
-       "3       0.087200   -0.575056   -0.244472      -1.225791     -1.208765   \n",
-       "4       1.689404    1.198610    0.117392       1.361744      1.160004   \n",
+       "0       0.451496   -0.863189    0.319180       0.268475     -0.438362   \n",
+       "1       1.954944    0.888255   -0.506518       0.916637      1.321011   \n",
+       "2      -0.060250   -1.857638    0.806913       0.635421      0.666008   \n",
+       "3      -1.167276   -0.164620    1.687004       0.854768     -0.227239   \n",
+       "4       1.715229   -0.439130   -0.238847       0.496327      1.359385   \n",
        "...          ...         ...         ...            ...           ...   \n",
-       "2495    1.057077   -0.803677   -0.759486      -0.611072     -1.013733   \n",
-       "2496    0.135980   -0.448317   -0.113937      -0.652803     -0.103194   \n",
-       "2497   -0.934772   -0.587933    1.399632      -1.077664     -1.793181   \n",
-       "2498   -1.475989    0.910175    0.325985       0.349857      0.066698   \n",
-       "2499   -0.375057    0.280377   -0.899183       0.315558      0.841183   \n",
+       "2495    0.178579    0.763186    0.527462      -0.464865     -0.618693   \n",
+       "2496    1.238957    0.957759   -0.583051      -2.916171     -2.283696   \n",
+       "2497   -0.288768   -0.861025    0.372657       0.586824      0.097788   \n",
+       "2498    1.345018    0.705361    0.414329      -1.010186     -0.893686   \n",
+       "2499    0.012787   -1.777783    0.340176       1.907981      1.581676   \n",
        "\n",
        "      T_cont_scaled  T_bin_scaled  \n",
-       "0          1.535974      1.001401  \n",
-       "1         -0.459080     -0.998202  \n",
-       "2          0.574396      1.001401  \n",
-       "3         -1.152913     -0.998202  \n",
-       "4          1.339552      1.001401  \n",
+       "0          0.347809     -1.022452  \n",
+       "1          0.726014      0.977650  \n",
+       "2          0.657767      0.977650  \n",
+       "3          1.304402      0.977650  \n",
+       "4          0.194070      0.977650  \n",
        "...             ...           ...  \n",
-       "2495      -0.167970      1.001401  \n",
-       "2496      -0.911497     -0.998202  \n",
-       "2497      -0.730043     -0.998202  \n",
-       "2498       0.557359      1.001401  \n",
-       "2499       0.194693      1.001401  \n",
+       "2495      -0.475800     -1.022452  \n",
+       "2496      -2.779944     -1.022452  \n",
+       "2497       0.511847     -1.022452  \n",
+       "2498      -1.028702     -1.022452  \n",
+       "2499       1.832247      0.977650  \n",
        "\n",
        "[2500 rows x 23 columns]"
       ]
@@ -494,12 +505,47 @@
     "data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e2472e18",
+   "metadata": {},
+   "source": [
+    "CausalPy's  `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world.\n",
+    "\n",
+    "### The Spike-and-Slab: Discrete Choices\n",
+    "\n",
+    "The spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n",
+    "\n",
+    "$$ \\beta_{j} = \\gamma_{j} \\cdot \\beta_{j_\\text{raw}}$$\n",
+    "\n",
+    "such that  \n",
+    "\n",
+    "$$ \\gamma_{j} \\in \\{0, 1\\}$$\n",
+    "\n",
+    "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
+    "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
+    "\n",
+    "### The Regularised Horseshoe: Continuos Moderation\n",
+    "\n",
+    "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
+    "\n",
+    "$$ \\beta_{j} = \\tau \\cdot \\lambda_{j} \\cdot \\beta_{j\\text{raw}}$$\n",
+    "\n",
+    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\lambda_{j}$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "806df6ea",
    "metadata": {},
    "source": [
-    "### Hyperparameters for Variable Selection Priors"
+    "### Hyperparameters for Variable Selection Priors\n",
+    "\n",
+    "You can control the behaviour of the variable selection priors through some of the hyperparameters available. For the spike and slab prior, the most important hyperparamers are `temperature`, `pi_alpha`, and `pi_beta`. \n",
+    "\n",
+    "Because our sampler doesn't like discrete variables, we're approximating a bernoulli outcome in our sampling to define the spike and slab. The approximation is governed by the `temperature` parameter. The default value of 0.1 works well in most cases, creating indicators that cluster near 0 or 1 without causing sampling difficulties.\n",
+    "\n",
+    "The selection probability parameters `pi_alpha` and `pi_beta` encode your prior belief about sparsity. With both set to 2 (the default), you're placing a Beta(2,2) prior on π, the overall proportion of selected variables. This is symmetric around 0.5 but slightly concentrated there—you're saying \"I don't know how many variables are relevant, but probably not all of them and probably not none of them.\""
    ]
   },
   {
@@ -510,7 +556,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "
"
       ]
@@ -527,9 +573,17 @@
     "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3237bb49",
+   "metadata": {},
+   "source": [
+    "We'll now fit two models and estimate the implied treatment effect."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "763ca253",
    "metadata": {},
    "outputs": [
@@ -553,16 +607,18 @@
       "                The coefficients should be interpreted appropriately.\n",
       "  warnings.warn(\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [beta_t, beta_z, chol_cov]\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6de2845a3ae7405c85006a8c991e5614",
+       "model_id": "b332c2837f1849329b3b561a9765f3e5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -577,6 +633,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
      ]
@@ -595,8 +657,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 99 seconds.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 133 seconds.\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
       "                This is not necessarily a problem but it violates\n",
@@ -622,16 +683,18 @@
      "output_type": "stream",
      "text": [
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [pi_beta_t, beta_t_raw, gamma_beta_t_u, pi_beta_z, beta_z_raw, gamma_beta_z_u, chol_cov]\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1fa39df9f8f40b4ae84c1d4783720c9",
+       "model_id": "db312f02efe743c386a4f2b4449f8904",
        "version_major": 2,
        "version_minor": 0
       },
@@ -646,6 +709,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
@@ -666,9 +733,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 461 seconds.\n",
-      "There were 79 divergences after tuning. Increase `target_accept` or reparameterize.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 551 seconds.\n",
+      "There were 167 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
    ],
@@ -676,8 +744,8 @@
     "sample_kwargs = {\n",
     "    \"draws\": 1000,\n",
     "    \"tune\": 2000,\n",
-    "    \"chains\": 2,\n",
-    "    \"cores\": 2,\n",
+    "    \"chains\": 4,\n",
+    "    \"cores\": 4,\n",
     "    \"target_accept\": 0.95,\n",
     "    \"progressbar\": True,\n",
     "    \"random_seed\": 42,\n",
@@ -722,25 +790,588 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2ccb6e0b",
+   "metadata": {},
+   "source": [
+    "The models have quite a distinct structure. Compare the normal IV model with non variable selection priors. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "838e0726",
+   "execution_count": 17,
+   "id": "e97a9ca2",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "clusterinstruments (15)\n",
+       "\n",
+       "instruments (15)\n",
+       "\n",
+       "\n",
+       "clustercovariates (16)\n",
+       "\n",
+       "covariates (16)\n",
+       "\n",
+       "\n",
+       "cluster3\n",
+       "\n",
+       "3\n",
+       "\n",
+       "\n",
+       "cluster2 x 2\n",
+       "\n",
+       "2 x 2\n",
+       "\n",
+       "\n",
+       "cluster2\n",
+       "\n",
+       "2\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t->mu_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z->mu_y\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
+       "\n",
+       "\n",
+       "\n",
+       "cov\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->cov\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_corr\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_corr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_stds\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_stds\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(result_normal.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34f3a1b7",
+   "metadata": {},
+   "source": [
+    "Now compare the structure of the spike and slab model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4f8c2685",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "clusterinstruments (15)\n",
+       "\n",
+       "instruments (15)\n",
+       "\n",
+       "\n",
+       "clustercovariates (16)\n",
+       "\n",
+       "covariates (16)\n",
+       "\n",
+       "\n",
+       "cluster3\n",
+       "\n",
+       "3\n",
+       "\n",
+       "\n",
+       "cluster2 x 2\n",
+       "\n",
+       "2 x 2\n",
+       "\n",
+       "\n",
+       "cluster2\n",
+       "\n",
+       "2\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z\n",
+       "\n",
+       "pi_beta_z\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z\n",
+       "\n",
+       "gamma_beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_t\n",
+       "\n",
+       "pi_beta_t\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t\n",
+       "\n",
+       "gamma_beta_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_t->gamma_beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw\n",
+       "\n",
+       "beta_t_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw->beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t->mu_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t->beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t_u->gamma_beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z->mu_y\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z->beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z_raw\n",
+       "\n",
+       "beta_z_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z_raw->beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
+       "\n",
+       "\n",
+       "\n",
+       "cov\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->cov\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_corr\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_corr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_stds\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_stds\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(result_spike_slab.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "368660c8",
+   "metadata": {},
+   "source": [
+    "Despite seeing some divergences in our spike and slab model, most other sampler health metrics seem healthy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0755095c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_energy(result_spike_slab.idata, figsize=(20, 6));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bffd8b6",
+   "metadata": {},
+   "source": [
+    "And since we know the true data generating conditions we can also assess the derived posterior treatment estimates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -752,28 +1383,42 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(20, 6))\n",
     "az.plot_posterior(\n",
-    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    "    result_normal.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    label=\"Normal\",\n",
     ")\n",
     "az.plot_posterior(\n",
     "    result_spike_slab.idata,\n",
     "    var_names=[\"beta_z\"],\n",
     "    coords={\"covariates\": [\"T_cont\"]},\n",
     "    ax=ax,\n",
+    "    color=\"green\",\n",
+    "    label=\"spike and slab\",\n",
     ")\n",
-    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
+    "ax.axvline(3, color=\"black\", linestyle=\"--\", label=\"True value\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057b4f5d",
+   "metadata": {},
+   "source": [
+    "This plot suggests that the spike and slab prior was better able to ignore noise in the process and zero in on the true effect. This will not always work but it is a sensible practice to at least sensitivity check difference between the estimates under different prior settings. We can observe how aggressively the spike and slab prior worked to cull unwanted variables from each model by comparing the values on the coefficients across each model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "127888b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -786,6 +1431,7 @@
     "    var_names=[\"beta_z\"],\n",
     "    combined=True,\n",
     "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    "    r_hat=True,\n",
     ")\n",
     "axs[0].set_title(\"Parameter Comparison Outcome Model \\n Baseline v Spike and Slab\");"
    ]
@@ -800,15 +1446,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "id": "acafc928",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -821,6 +1467,7 @@
     "    var_names=[\"beta_t\"],\n",
     "    combined=True,\n",
     "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    "    r_hat=True,\n",
     ")\n",
     "\n",
     "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
@@ -828,7 +1475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "f2a0b213",
    "metadata": {},
    "outputs": [
@@ -861,125 +1508,125 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.0105\n",
+       "      0.01750\n",
        "      False\n",
-       "      0.014042\n",
+       "      0.020546\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      1.0000\n",
+       "      1.00000\n",
        "      True\n",
-       "      0.990424\n",
+       "      0.991285\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.7605\n",
+       "      0.64100\n",
        "      True\n",
-       "      0.775804\n",
+       "      0.659357\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.6880\n",
+       "      0.58200\n",
        "      True\n",
-       "      0.719690\n",
+       "      0.616580\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.0570\n",
+       "      0.17625\n",
        "      False\n",
-       "      0.064343\n",
+       "      0.192949\n",
        "    \n",
        "    \n",
        "      5\n",
-       "      0.0330\n",
+       "      0.06075\n",
        "      False\n",
-       "      0.038359\n",
+       "      0.068297\n",
        "    \n",
        "    \n",
        "      6\n",
-       "      0.6755\n",
+       "      0.66600\n",
        "      True\n",
-       "      0.704165\n",
+       "      0.702671\n",
        "    \n",
        "    \n",
        "      7\n",
-       "      0.0085\n",
+       "      0.01000\n",
        "      False\n",
-       "      0.011460\n",
+       "      0.012679\n",
        "    \n",
        "    \n",
        "      8\n",
-       "      0.0170\n",
+       "      0.01300\n",
        "      False\n",
-       "      0.021624\n",
+       "      0.016342\n",
        "    \n",
        "    \n",
        "      9\n",
-       "      0.0130\n",
+       "      0.00975\n",
        "      False\n",
-       "      0.015590\n",
+       "      0.012392\n",
        "    \n",
        "    \n",
        "      10\n",
-       "      0.0080\n",
+       "      0.01700\n",
        "      False\n",
-       "      0.010675\n",
+       "      0.019508\n",
        "    \n",
        "    \n",
        "      11\n",
-       "      0.0250\n",
+       "      0.01625\n",
        "      False\n",
-       "      0.029619\n",
+       "      0.021217\n",
        "    \n",
        "    \n",
        "      12\n",
-       "      0.0060\n",
+       "      0.02000\n",
        "      False\n",
-       "      0.008689\n",
+       "      0.024469\n",
        "    \n",
        "    \n",
        "      13\n",
-       "      0.0250\n",
+       "      0.01825\n",
        "      False\n",
-       "      0.028057\n",
+       "      0.023474\n",
        "    \n",
        "    \n",
        "      14\n",
-       "      0.0135\n",
+       "      0.02700\n",
        "      False\n",
-       "      0.016080\n",
+       "      0.031636\n",
        "    \n",
        "    \n",
        "      15\n",
-       "      0.0135\n",
+       "      0.01650\n",
        "      False\n",
-       "      0.015825\n",
+       "      0.020863\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "      prob  selected  gamma_mean\n",
-       "0   0.0105     False    0.014042\n",
-       "1   1.0000      True    0.990424\n",
-       "2   0.7605      True    0.775804\n",
-       "3   0.6880      True    0.719690\n",
-       "4   0.0570     False    0.064343\n",
-       "5   0.0330     False    0.038359\n",
-       "6   0.6755      True    0.704165\n",
-       "7   0.0085     False    0.011460\n",
-       "8   0.0170     False    0.021624\n",
-       "9   0.0130     False    0.015590\n",
-       "10  0.0080     False    0.010675\n",
-       "11  0.0250     False    0.029619\n",
-       "12  0.0060     False    0.008689\n",
-       "13  0.0250     False    0.028057\n",
-       "14  0.0135     False    0.016080\n",
-       "15  0.0135     False    0.015825"
+       "       prob  selected  gamma_mean\n",
+       "0   0.01750     False    0.020546\n",
+       "1   1.00000      True    0.991285\n",
+       "2   0.64100      True    0.659357\n",
+       "3   0.58200      True    0.616580\n",
+       "4   0.17625     False    0.192949\n",
+       "5   0.06075     False    0.068297\n",
+       "6   0.66600      True    0.702671\n",
+       "7   0.01000     False    0.012679\n",
+       "8   0.01300     False    0.016342\n",
+       "9   0.00975     False    0.012392\n",
+       "10  0.01700     False    0.019508\n",
+       "11  0.01625     False    0.021217\n",
+       "12  0.02000     False    0.024469\n",
+       "13  0.01825     False    0.023474\n",
+       "14  0.02700     False    0.031636\n",
+       "15  0.01650     False    0.020863"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1001,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 9,
    "id": "63edfa4e",
    "metadata": {},
    "outputs": [
@@ -1027,16 +1674,18 @@
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
       "  warnings.warn(\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [tau_beta_t, lambda_beta_t, c2_beta_t, beta_t_raw, tau_beta_z, lambda_beta_z, c2_beta_z, beta_z_raw, chol_cov]\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1407969a4c4946d0920f0defc2ec153c",
+       "model_id": "d795e2e885b74a708646d93e4d96f152",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1051,6 +1700,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
@@ -1071,10 +1724,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 743 seconds.\n",
-      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
-      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 644 seconds.\n",
+      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
@@ -1099,10 +1750,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 10,
    "id": "9c283ee1",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[autoreload of cutils_ext failed: Traceback (most recent call last):\n",
+      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 325, in check\n",
+      "    superreload(m, reload, self.old_objects)\n",
+      "    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 580, in superreload\n",
+      "    module = reload(module)\n",
+      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/importlib/__init__.py\", line 128, in reload\n",
+      "    raise ModuleNotFoundError(f\"spec not found for the module {name!r}\", name=name)\n",
+      "ModuleNotFoundError: spec not found for the module 'cutils_ext'\n",
+      "]\n"
+     ]
+    },
     {
      "data": {
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        "    \n",
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        "      1\n",
-       "      1.574053\n",
-       "      75.808614\n",
-       "      0.031037\n",
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        "    \n",
        "    \n",
        "      2\n",
-       "      0.454083\n",
-       "      21.869287\n",
-       "      0.031037\n",
+       "      0.164625\n",
+       "      12.062918\n",
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        "    \n",
        "    \n",
        "      3\n",
-       "      0.416588\n",
-       "      20.063451\n",
-       "      0.031037\n",
+       "      0.136282\n",
+       "      9.986066\n",
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        "    \n",
        "    \n",
        "      4\n",
-       "      0.078152\n",
-       "      3.763922\n",
-       "      0.031037\n",
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+       "      6.658563\n",
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        "    \n",
        "    \n",
        "      5\n",
-       "      0.043892\n",
-       "      2.113912\n",
-       "      0.031037\n",
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+       "      2.216801\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      6\n",
-       "      0.388261\n",
-       "      18.699190\n",
-       "      0.031037\n",
+       "      0.199524\n",
+       "      14.620131\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      7\n",
-       "      0.027243\n",
-       "      1.312060\n",
-       "      0.031037\n",
+       "      0.015415\n",
+       "      1.129522\n",
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        "    \n",
        "    \n",
        "      8\n",
-       "      0.027663\n",
-       "      1.332293\n",
-       "      0.031037\n",
+       "      0.015330\n",
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        "    \n",
        "    \n",
        "      9\n",
-       "      0.026640\n",
-       "      1.283037\n",
-       "      0.031037\n",
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        "      10\n",
-       "      0.026496\n",
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-       "      0.031037\n",
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        "    \n",
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        "      11\n",
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-       "      0.031037\n",
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        "    \n",
        "    \n",
        "      12\n",
-       "      0.024832\n",
-       "      1.195942\n",
-       "      0.031037\n",
+       "      0.021961\n",
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        "    \n",
        "    \n",
        "      13\n",
-       "      0.032776\n",
-       "      1.578531\n",
-       "      0.031037\n",
+       "      0.020631\n",
+       "      1.511758\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      14\n",
-       "      0.026933\n",
-       "      1.297110\n",
-       "      0.031037\n",
+       "      0.021207\n",
+       "      1.553953\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      15\n",
-       "      0.024878\n",
-       "      1.198152\n",
-       "      0.031037\n",
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        "    \n",
        "  \n",
        "\n",
@@ -1232,25 +1899,25 @@
       ],
       "text/plain": [
        "    shrinkage_factor  lambda_tilde       tau\n",
-       "0           0.024168      1.163944  0.031037\n",
-       "1           1.574053     75.808614  0.031037\n",
-       "2           0.454083     21.869287  0.031037\n",
-       "3           0.416588     20.063451  0.031037\n",
-       "4           0.078152      3.763922  0.031037\n",
-       "5           0.043892      2.113912  0.031037\n",
-       "6           0.388261     18.699190  0.031037\n",
-       "7           0.027243      1.312060  0.031037\n",
-       "8           0.027663      1.332293  0.031037\n",
-       "9           0.026640      1.283037  0.031037\n",
-       "10          0.026496      1.276087  0.031037\n",
-       "11          0.031909      1.536788  0.031037\n",
-       "12          0.024832      1.195942  0.031037\n",
-       "13          0.032776      1.578531  0.031037\n",
-       "14          0.026933      1.297110  0.031037\n",
-       "15          0.024878      1.198152  0.031037"
+       "0           0.016681      1.222294  0.038199\n",
+       "1           0.847573     62.105904  0.038199\n",
+       "2           0.164625     12.062918  0.038199\n",
+       "3           0.136282      9.986066  0.038199\n",
+       "4           0.090871      6.658563  0.038199\n",
+       "5           0.030253      2.216801  0.038199\n",
+       "6           0.199524     14.620131  0.038199\n",
+       "7           0.015415      1.129522  0.038199\n",
+       "8           0.015330      1.123328  0.038199\n",
+       "9           0.015992      1.171778  0.038199\n",
+       "10          0.015676      1.148635  0.038199\n",
+       "11          0.020536      1.504805  0.038199\n",
+       "12          0.021961      1.609202  0.038199\n",
+       "13          0.020631      1.511758  0.038199\n",
+       "14          0.021207      1.553953  0.038199\n",
+       "15          0.019708      1.444109  0.038199"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,23 +1931,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 14,
    "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 134,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]

From 896ee5760dae71d9827501275ad36e8621130553 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 21:25:09 +0000
Subject: [PATCH 12/24] update index

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/index.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/notebooks/index.md b/docs/source/notebooks/index.md
index c9ae0ad7..be879e33 100644
--- a/docs/source/notebooks/index.md
+++ b/docs/source/notebooks/index.md
@@ -65,6 +65,7 @@ rkink_pymc.ipynb
 
 iv_pymc.ipynb
 iv_weak_instruments.ipynb
+iv_vs_priors.ipynb
 :::
 
 :::{toctree}

From 068c92242cd528f0a6e2df413b40e8b4c4c1cd2b Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 21:49:08 +0000
Subject: [PATCH 13/24] update spelling

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index bd12eed6..3d4af100 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -525,7 +525,7 @@
     "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
     "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
     "\n",
-    "### The Regularised Horseshoe: Continuos Moderation\n",
+    "### The Regularised Horseshoe: Gentle Moderation\n",
     "\n",
     "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
     "\n",

From 13b320e6652c39ff71a6853fa72b25b62668609d Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Sat, 22 Nov 2025 22:00:25 +0000
Subject: [PATCH 14/24] add binary treatment case to IV model

Signed-off-by: Nathaniel 
---
 causalpy/experiments/instrumental_variable.py |  34 ++++--
 causalpy/pymc_models.py                       | 105 +++++++++++++-----
 .../tests/test_integration_pymc_examples.py   |  31 ++++++
 docs/source/_static/interrogate_badge.svg     |   6 +-
 4 files changed, 136 insertions(+), 40 deletions(-)

diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py
index 5b6b4128..4fdf7429 100644
--- a/causalpy/experiments/instrumental_variable.py
+++ b/causalpy/experiments/instrumental_variable.py
@@ -57,6 +57,10 @@ class InstrumentalVariable(BaseExperiment):
     vs_hyperparams : dict, optional
         Hyperparameters for variable selection priors. Only used if vs_prior_type
         is not None.
+    binary_treatment : bool, default=False
+        A indicator for whether the treatment to be modelled is binary or not.
+        Determines which PyMC model we use to model the joint outcome and
+        treatment.
 
     Example
     --------
@@ -116,6 +120,7 @@ def __init__(
         priors: dict | None = None,
         vs_prior_type=None,
         vs_hyperparams=None,
+        binary_treatment=False,
         **kwargs: dict,
     ) -> None:
         super().__init__(model=model)
@@ -127,6 +132,7 @@ def __init__(
         self.model = model
         self.vs_prior_type = vs_prior_type
         self.vs_hyperparams = vs_hyperparams or {}
+        self.binary_treatment = binary_treatment
         self.input_validation()
 
         y, X = dmatrices(formula, self.data)
@@ -150,12 +156,22 @@ def __init__(
         COORDS = {"instruments": self.labels_instruments, "covariates": self.labels}
         self.coords = COORDS
         if priors is None:
-            priors = {
-                "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
-                "sigmas": [10, 10],
-                "eta": 2,
-                "lkj_sd": 1,
-            }
+            if binary_treatment:
+                # Different default priors for binary treatment
+                priors = {
+                    "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
+                    "sigmas": [1, 1],
+                    "sigma_U": 1.0,
+                    "rho_bounds": [-0.99, 0.99],
+                }
+            else:
+                # Original continuous treatment priors
+                priors = {
+                    "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
+                    "sigmas": [1, 1],
+                    "eta": 2,
+                    "lkj_sd": 1,
+                }
         self.priors = priors
         self.model.fit(  # type: ignore[call-arg,union-attr]
             X=self.X,
@@ -166,6 +182,7 @@ def __init__(
             priors=self.priors,
             vs_prior_type=vs_prior_type,
             vs_hyperparams=vs_hyperparams,
+            binary_treatment=self.binary_treatment,
         )
 
     def input_validation(self) -> None:
@@ -186,9 +203,8 @@ def input_validation(self) -> None:
         if check_binary:
             warnings.warn(
                 """Warning. The treatment variable is not Binary.
-                This is not necessarily a problem but it violates
-                the assumption of a simple IV experiment.
-                The coefficients should be interpreted appropriately."""
+                We will use the multivariate normal likelihood
+                for continuous treatment."""
             )
 
     def get_2SLS_fit(self) -> None:
diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index 1c0b0a0f..71f5210e 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -683,6 +683,7 @@ def build_model(  # type: ignore
         priors,
         vs_prior_type=None,
         vs_hyperparams=None,
+        binary_treatment=False,
     ) -> None:
         """Specify model with treatment regression and focal regression
         data and priors.
@@ -709,6 +710,8 @@ def build_model(  # type: ignore
                               or "horseshoe" or "normal
         vs_hyperparams: An optional dictionary of priors for the
                                variable selection hyperparameters
+        binary_treatment: A flag for determining the relevant
+                                likelihood to be used.
 
         """
 
@@ -755,31 +758,74 @@ def build_model(  # type: ignore
                     dims="covariates",
                 )
 
-            sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2)
-            chol, _, _ = pm.LKJCholeskyCov(
-                name="chol_cov",
-                eta=priors["eta"],
-                n=2,
-                sd_dist=sd_dist,
-            )
-            # compute and store the covariance matrix
-            pm.Deterministic(name="cov", var=pt.dot(l=chol, r=chol.T))
-
-            # --- Parameterization ---
-            mu_y = pm.Deterministic(name="mu_y", var=pt.dot(X, beta_z))
-            # focal regression
-            mu_t = pm.Deterministic(name="mu_t", var=pt.dot(Z, beta_t))
-            # instrumental regression
-            mu = pm.Deterministic(name="mu", var=pt.stack(tensors=(mu_y, mu_t), axis=1))
-
-            # --- Likelihood ---
-            pm.MvNormal(
-                name="likelihood",
-                mu=mu,
-                chol=chol,
-                observed=np.stack(arrays=(y.flatten(), t.flatten()), axis=1),
-                shape=(X.shape[0], 2),
-            )
+            if binary_treatment:
+                # Binary treatment formulation with correlated latent errors
+                sigma_U = pm.Exponential("sigma_U", priors.get("sigma_U", 1.0))
+
+                # Correlation parameter with bounds
+                rho_lower = priors.get("rho_bounds", [-0.99, 0.99])[0]
+                rho_upper = priors.get("rho_bounds", [-0.99, 0.99])[1]
+
+                # Use tanh transform to keep correlation in valid range
+                rho_unconstr = pm.Normal("rho_unconstr", 0, 0.5)
+                rho = pm.Deterministic("rho", pm.math.tanh(rho_unconstr))
+
+                # Clip to ensure numerical stability
+                rho_clipped = pt.clip(rho, rho_lower + 0.01, rho_upper - 0.01)
+
+                # Cholesky decomposition for correlated errors
+                inverse_rho = pm.math.sqrt(pm.math.maximum(1 - rho_clipped**2, 1e-12))
+                chol = pt.stack([[sigma_U, 0.0], [sigma_U * rho_clipped, inverse_rho]])
+
+                # Draw latent errors
+                eps_raw = pm.Normal("eps_raw", 0, 1, shape=(X.shape[0], 2))
+                eps = pm.Deterministic("eps", pt.dot(eps_raw, chol.T))
+
+                U = eps[:, 0]  # Outcome error
+                V = eps[:, 1]  # Treatment error
+
+                # Treatment equation (logit link for binary treatment)
+                mu_treatment = pm.Deterministic("mu_t", pt.dot(Z, beta_t) + V)
+                p_t = pm.math.invlogit(mu_treatment)
+                pm.Bernoulli("likelihood_treatment", p=p_t, observed=t.flatten())
+
+                # Outcome equation
+                mu_outcome = pm.Deterministic("mu_y", pt.dot(X, beta_z) + U)
+                pm.Normal(
+                    "likelihood_outcome",
+                    mu=mu_outcome,
+                    sigma=sigma_U,
+                    observed=y.flatten(),
+                )
+
+            else:
+                sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2)
+                chol, _, _ = pm.LKJCholeskyCov(
+                    name="chol_cov",
+                    eta=priors["eta"],
+                    n=2,
+                    sd_dist=sd_dist,
+                )
+                # compute and store the covariance matrix
+                pm.Deterministic(name="cov", var=pt.dot(l=chol, r=chol.T))
+
+                # --- Parameterization ---
+                mu_y = pm.Deterministic(name="mu_y", var=pt.dot(X, beta_z))
+                # focal regression
+                mu_t = pm.Deterministic(name="mu_t", var=pt.dot(Z, beta_t))
+                # instrumental regression
+                mu = pm.Deterministic(
+                    name="mu", var=pt.stack(tensors=(mu_y, mu_t), axis=1)
+                )
+
+                # --- Likelihood ---
+                pm.MvNormal(
+                    name="likelihood",
+                    mu=mu,
+                    chol=chol,
+                    observed=np.stack(arrays=(y.flatten(), t.flatten()), axis=1),
+                    shape=(X.shape[0], 2),
+                )
 
     def sample_predictive_distribution(self, ppc_sampler: str | None = "jax") -> None:
         """Function to sample the Multivariate Normal posterior predictive
@@ -813,7 +859,7 @@ def sample_predictive_distribution(self, ppc_sampler: str | None = "jax") -> Non
                         )
                     )
 
-    def fit(
+    def fit(  # type: ignore[override]
         self,
         X,
         Z,
@@ -824,7 +870,8 @@ def fit(
         ppc_sampler=None,
         vs_prior_type=None,
         vs_hyperparams=None,
-    ):
+        binary_treatment: bool = False,
+    ):  # type: ignore[override]
         """Draw samples from posterior distribution and potentially
         from the prior and posterior predictive distributions. The
         fit call can take values for the
@@ -838,7 +885,9 @@ def fit(
         # sample_posterior_predictive() if provided in sample_kwargs.
         # Use JAX for ppc sampling of multivariate likelihood
 
-        self.build_model(X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams)
+        self.build_model(
+            X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams, binary_treatment
+        )
         with self:
             self.idata = pm.sample(**self.sample_kwargs)
         self.sample_predictive_distribution(ppc_sampler=ppc_sampler)
diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py
index 585c0b1f..6675a298 100644
--- a/causalpy/tests/test_integration_pymc_examples.py
+++ b/causalpy/tests/test_integration_pymc_examples.py
@@ -682,6 +682,37 @@ def test_iv_reg(mock_pymc_sample):
         result.get_plot_data()
 
 
+@pytest.mark.integration
+def test_iv_binary_treatment(mock_pymc_sample):
+    df = cp.load_data("risk")
+    df["binary_trt"] = np.random.binomial(1, 0.5, len(df))
+    instruments_formula = "binary_trt  ~ 1 + risk + logmort0"
+    formula = "loggdp ~  1 + binary_trt + risk"
+    instruments_data = df[["risk", "logmort0", "binary_trt"]]
+    data = df[["loggdp", "risk", "binary_trt"]]
+
+    result = cp.InstrumentalVariable(
+        instruments_data=instruments_data,
+        data=data,
+        instruments_formula=instruments_formula,
+        formula=formula,
+        model=cp.pymc_models.InstrumentalVariableRegression(
+            sample_kwargs=sample_kwargs
+        ),
+        binary_treatment=True,
+    )
+    result.model.sample_predictive_distribution(ppc_sampler="pymc")
+    assert isinstance(df, pd.DataFrame)
+    assert isinstance(data, pd.DataFrame)
+    assert isinstance(instruments_data, pd.DataFrame)
+    assert isinstance(result, cp.InstrumentalVariable)
+    assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"]
+    assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
+    with pytest.raises(NotImplementedError):
+        result.get_plot_data()
+    assert "rho" in result.model.named_vars
+
+
 @pytest.mark.integration
 def test_iv_reg_vs_prior(mock_pymc_sample):
     df = cp.load_data("risk")
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index 5fef2054..4ed4f3af 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,5 +1,5 @@
 
-    interrogate: 94.8%
+    interrogate: 94.6%
     
         
             
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        94.8%
-        94.8%
+        94.6%
+        94.6%
     
     
         

From 62101166348b37ee28d8c88eae143e1975fdd33f Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Wed, 26 Nov 2025 21:34:30 +0000
Subject: [PATCH 15/24] adding more write up

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 426 +++++++++++++++++------
 1 file changed, 318 insertions(+), 108 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 3d4af100..3e293ad5 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -512,7 +512,7 @@
    "source": [
     "CausalPy's  `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world.\n",
     "\n",
-    "### The Spike-and-Slab: Discrete Choices\n",
+    "#### The Spike-and-Slab: Discrete Choices\n",
     "\n",
     "The spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n",
     "\n",
@@ -525,13 +525,13 @@
     "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
     "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
     "\n",
-    "### The Regularised Horseshoe: Gentle Moderation\n",
+    "#### The Regularised Horseshoe: Gentle Moderation\n",
     "\n",
     "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
     "\n",
-    "$$ \\beta_{j} = \\tau \\cdot \\lambda_{j} \\cdot \\beta_{j\\text{raw}}$$\n",
+    "$$ \\beta_{j} = \\tau \\cdot \\tilde{\\lambda}_j \\cdot \\beta_{j\\text{raw}}$$\n",
     "\n",
-    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\lambda_{j}$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
+    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\tilde{\\lambda}_j$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
    ]
   },
   {
@@ -550,13 +550,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 52,
    "id": "ae848fe9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -570,7 +570,8 @@
     "axs = axs.flatten()\n",
     "axs[0].hist(pm.draw(pm.Beta.dist(2, 2), 1000), ec=\"black\", color=\"slateblue\")\n",
     "axs[1].hist(pm.draw(pm.Beta.dist(2, 5), 1000), ec=\"black\", color=\"slateblue\")\n",
-    "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\");"
+    "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\")\n",
+    "axs[1].set_title(r\"Various Distributions for the $\\pi$ hyperparameter\", size=20);"
    ]
   },
   {
@@ -583,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "763ca253",
    "metadata": {},
    "outputs": [
@@ -782,11 +783,7 @@
     "    formula=formula,\n",
     "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
     "    vs_prior_type=\"spike_and_slab\",\n",
-    "    vs_hyperparams={\n",
-    "        \"pi_alpha\": 2,\n",
-    "        \"pi_beta\": 2,\n",
-    "        \"slab_sigma\": 2,\n",
-    "    },\n",
+    "    vs_hyperparams={\"pi_alpha\": 2, \"pi_beta\": 2, \"slab_sigma\": 2, \"temperature\": 0.1},\n",
     ")"
    ]
   },
@@ -1473,6 +1470,14 @@
     "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "07f7d95b",
+   "metadata": {},
+   "source": [
+    "The spike and slab prior can also output direct inclusion probabilities that can be used for communication regarding which variables were \"selected\" in the process."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -1643,12 +1648,63 @@
    "id": "38568d27",
    "metadata": {},
    "source": [
-    "## Horseshoe"
+    "### Horseshoe\n",
+    "\n",
+    "The horseshoe prior is a sophisticated continuous shrinkage method designed for regularization and variable selection in high-dimensional regression settings. Unlike discrete selection approaches, it operates through a elegant hierarchical structure that adaptively shrinks coefficients based on the strength of their signal in the data. The key to the implementation is the hierarchical $\\lambda$ component: \n",
+    "\n",
+    "$$ \\tilde{\\lambda}_j = \\sqrt{\\frac{c^2 \\lambda_j^2}{c^2 + \\tau^2 \\lambda_j^2}} $$\n",
+    "\n",
+    "is composed of individual local shrinkage parameters and $c^2$ is a regularization parameter that prevents over-shrinkage of genuinely large signals. \n",
+    "\n",
+    "#### The $\\tau_0$ hyperparameter\n",
+    "\n",
+    "Like the `temperature` parameter in the spike and slab model, the $\\tau_0$ parameter determines the overall level of sparsity expected in the model. However, the $tau_0$ will by default be derived from the data and the number of covariates in your data. While both the horseshoe and spike-and-slab priors address variable selection and sparsity, they embody fundamentally different philosophies about how to achieve these goals. The horseshoe embraces continuity, creating a smooth gradient of shrinkage where all coefficients remain in the model but are pulled toward zero with varying intensity. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 51,
+   "id": "16bb5f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "axs[0].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(2, 2), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[1].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(3, 3), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[2].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(4, 4), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[1].set_title(r\"Various Distributions for the $c^{2}$ hyperparameter\", size=20);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "id": "63edfa4e",
    "metadata": {},
    "outputs": [
@@ -1685,7 +1741,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d795e2e885b74a708646d93e4d96f152",
+       "model_id": "1410b52c995348e397aed1a2843f2bb7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1724,8 +1780,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 644 seconds.\n",
-      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 534 seconds.\n",
+      "There were 16 divergences after tuning. Increase `target_accept` or reparameterize.\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
@@ -1745,31 +1801,24 @@
     "    formula=formula,\n",
     "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
     "    vs_prior_type=\"horseshoe\",\n",
+    "    vs_hyperparams={\"c2_alpha\": 3, \"c2_beta\": 3},\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "db7d86aa",
+   "metadata": {},
+   "source": [
+    "Similar to the inclusion probabilities in the spike and slab model, a horseshoe model can output the relative shrinkage factor that gets applied to each variables inclusion. This method of variable is less decisive than spike and slab, but also mitigates case of completely zero-ing the small but real contributions of certain variables."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 41,
    "id": "9c283ee1",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[autoreload of cutils_ext failed: Traceback (most recent call last):\n",
-      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 325, in check\n",
-      "    superreload(m, reload, self.old_objects)\n",
-      "    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 580, in superreload\n",
-      "    module = reload(module)\n",
-      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/importlib/__init__.py\", line 128, in reload\n",
-      "    raise ModuleNotFoundError(f\"spec not found for the module {name!r}\", name=name)\n",
-      "ModuleNotFoundError: spec not found for the module 'cutils_ext'\n",
-      "]\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
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        "    
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        "    
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        "      1\n",
-       "      0.847573\n",
-       "      62.105904\n",
-       "      0.038199\n",
+       "      0.697739\n",
+       "      57.713961\n",
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        "    
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        "    
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        "      2\n",
-       "      0.164625\n",
-       "      12.062918\n",
-       "      0.038199\n",
+       "      0.160282\n",
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        "    
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        "    
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        "      3\n",
-       "      0.136282\n",
-       "      9.986066\n",
-       "      0.038199\n",
+       "      0.129649\n",
+       "      10.724023\n",
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        "    
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        "    
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        "      4\n",
-       "      0.090871\n",
-       "      6.658563\n",
-       "      0.038199\n",
+       "      0.076676\n",
+       "      6.342298\n",
+       "      0.036811\n",
        "    
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        "    
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        "      5\n",
-       "      0.030253\n",
-       "      2.216801\n",
-       "      0.038199\n",
+       "      0.025650\n",
+       "      2.121680\n",
+       "      0.036811\n",
        "    
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        "    
\n",
        "      6\n",
-       "      0.199524\n",
-       "      14.620131\n",
-       "      0.038199\n",
+       "      0.190186\n",
+       "      15.731398\n",
+       "      0.036811\n",
        "    
\n",
        "    
\n",
        "      7\n",
-       "      0.015415\n",
-       "      1.129522\n",
-       "      0.038199\n",
+       "      0.014198\n",
+       "      1.174382\n",
+       "      0.036811\n",
        "    
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        "    
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        "      8\n",
-       "      0.015330\n",
-       "      1.123328\n",
-       "      0.038199\n",
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+       "      0.036811\n",
        "    
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        "    
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        "      9\n",
-       "      0.015992\n",
-       "      1.171778\n",
-       "      0.038199\n",
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        "    
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        "    
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        "    
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        "      11\n",
-       "      0.020536\n",
-       "      1.504805\n",
-       "      0.038199\n",
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+       "      0.036811\n",
        "    
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        "    
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        "      12\n",
-       "      0.021961\n",
-       "      1.609202\n",
-       "      0.038199\n",
+       "      0.018518\n",
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+       "      0.036811\n",
        "    
\n",
        "    
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        "      13\n",
-       "      0.020631\n",
-       "      1.511758\n",
-       "      0.038199\n",
+       "      0.017415\n",
+       "      1.440514\n",
+       "      0.036811\n",
        "    
\n",
        "    
\n",
        "      14\n",
-       "      0.021207\n",
-       "      1.553953\n",
-       "      0.038199\n",
+       "      0.019238\n",
+       "      1.591291\n",
+       "      0.036811\n",
        "    
\n",
        "    
\n",
        "      15\n",
-       "      0.019708\n",
-       "      1.444109\n",
-       "      0.038199\n",
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+       "      1.445946\n",
+       "      0.036811\n",
        "    
\n",
        "  \n",
        "\n",
@@ -1899,25 +1948,25 @@
       ],
       "text/plain": [
        "    shrinkage_factor  lambda_tilde       tau\n",
-       "0           0.016681      1.222294  0.038199\n",
-       "1           0.847573     62.105904  0.038199\n",
-       "2           0.164625     12.062918  0.038199\n",
-       "3           0.136282      9.986066  0.038199\n",
-       "4           0.090871      6.658563  0.038199\n",
-       "5           0.030253      2.216801  0.038199\n",
-       "6           0.199524     14.620131  0.038199\n",
-       "7           0.015415      1.129522  0.038199\n",
-       "8           0.015330      1.123328  0.038199\n",
-       "9           0.015992      1.171778  0.038199\n",
-       "10          0.015676      1.148635  0.038199\n",
-       "11          0.020536      1.504805  0.038199\n",
-       "12          0.021961      1.609202  0.038199\n",
-       "13          0.020631      1.511758  0.038199\n",
-       "14          0.021207      1.553953  0.038199\n",
-       "15          0.019708      1.444109  0.038199"
+       "0           0.014780      1.222524  0.036811\n",
+       "1           0.697739     57.713961  0.036811\n",
+       "2           0.160282     13.257798  0.036811\n",
+       "3           0.129649     10.724023  0.036811\n",
+       "4           0.076676      6.342298  0.036811\n",
+       "5           0.025650      2.121680  0.036811\n",
+       "6           0.190186     15.731398  0.036811\n",
+       "7           0.014198      1.174382  0.036811\n",
+       "8           0.014636      1.210634  0.036811\n",
+       "9           0.014024      1.160002  0.036811\n",
+       "10          0.014441      1.194485  0.036811\n",
+       "11          0.018907      1.563934  0.036811\n",
+       "12          0.018518      1.531734  0.036811\n",
+       "13          0.017415      1.440514  0.036811\n",
+       "14          0.019238      1.591291  0.036811\n",
+       "15          0.017481      1.445946  0.036811"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1931,23 +1980,149 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 53,
    "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       ""
+       "
"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_horseshoe.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    color=\"green\",\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e15d4f1e",
+   "metadata": {},
+   "source": [
+    "In this case it seems the horseshoe prior leads a bi-modal posterior estimate of the treatment effect suggesting a kind of indecision about the level of sparsity to apply. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc265f5d",
+   "metadata": {},
+   "source": [
+    "## Binary Treatment Case\n",
+    "\n",
+    "Our data generating function output two different simulation scenarios, where the treatment was either continuous or binary. This allows us to demonstrate the joint modelling of the binary treatment outcome which uses a Bernoulli likelihood for the treatment variable and latent confounding to model the joint realisation of treatment and outcome. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "89e61d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 1: Normal Priors Binary Treatment (No Variable Selection)\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_t, beta_z, sigma_U, rho_unconstr, eps_raw]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7276bf767824813a312c4c3ccff7c01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
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",
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 68 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "formula = \"Y_bin ~ T_bin + \" + \" + \".join(features)\n",
+    "instruments_formula = \"T_bin ~ 1 + \" + \" + \".join(features)\n",
+    "\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 1: Normal priors (no selection)\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 1: Normal Priors Binary Treatment (No Variable Selection)\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_normal_binary = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=None,  # No variable selection\n",
+    "    binary_treatment=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "98c1b50a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -1962,12 +2137,47 @@
     "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
     ")\n",
     "az.plot_posterior(\n",
-    "    result_horseshoe.idata,\n",
+    "    result_normal_binary.idata,\n",
     "    var_names=[\"beta_z\"],\n",
-    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    coords={\"covariates\": [\"T_bin\"]},\n",
     "    ax=ax,\n",
+    "    color=\"green\",\n",
     ")\n",
-    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
+    "ax.axvline(3, color=\"black\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ad018b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Conclusion: Choosing Your Path Through Uncertainty\n",
+    "\n",
+    "Variable selection priors offer a principled way to navigate the tension between model complexity and causal identification. Rather than forcing binary decisions about which variables to include, these priors encode our uncertainty about variable relevance directly into the inferential framework. But as we've seen, the choice between spike-and-slab and horseshoe reflects deeper commitments about how sparsity manifests in the world.\n",
+    "\n",
+    "**The spike-and-slab prior** embodies decisiveness. It asks: which variables truly matter? By pushing coefficients toward exactly zero or allowing them to take on substantial values, it produces interpretable inclusion probabilities that clearly communicate which predictors the model has \"selected.\" This approach shines when you believe that many potential confounders are genuine noise—included out of caution but ultimately irrelevant to the causal mechanism. The discrete nature of selection also makes results easier to communicate to stakeholders who think in terms of \"what factors matter?\" \n",
+    "\n",
+    "**The horseshoe prior** embraces nuance. It acknowledges that effects exist on a continuum, and that small but real contributions shouldn't be entirely zeroed out. The continuous shrinkage allows weak signals to persist (heavily damped) while strong signals emerge largely unscathed. This is valuable when you suspect that multiple confounders have genuine but varying degrees of influence, and when premature exclusion of any single variable might introduce bias. The regularization parameter $c^2$ acts as a safeguard, preventing even the horseshoe's aggressive shrinkage from overwhelming genuinely large effects.\n",
+    " \n",
+    "In our simulations, both approaches identified the true treatment effect of 3, though they arrived there differently. The spike-and-slab showed more confidence, producing tighter posterior intervals by decisively excluding noise variables. The horseshoe's bi-modal posterior in some specifications revealed its uncertainty about the appropriate level of sparsity a kind of probabilistic humility that spike-and-slab's discrete choices don't allow.\n",
+    "\n",
+    "**Practical Guidance:**\n",
+    " \n",
+    "- **Use spike-and-slab when** you have strong priors about sparsity (many potential confounders, few true ones), when interpretability matters (stakeholders want to know \"what's included?\"), or when you're willing to trade some flexibility for more decisive inference.\n",
+    " \n",
+    "- **Use horseshoe when** you're uncertain about sparsity levels, when small effects might still matter for causal identification, or when you want the model to smoothly adapt its shrinkage to the data without hard inclusion/exclusion decisions.\n",
+    " \n",
+    "- **Use neither when** theory clearly identifies your confounders, when sample size is large relative to the number of predictors, or when the cost of Type I errors (including irrelevant variables) is low relative to Type II errors (excluding true confounders).\n",
+    " \n",
+    "**Final Thoughts:**\n",
+    " \n",
+    "Variable selection priors don't eliminate the need for causal reasoning. They don't tell you which variables are *causally* relevant, only which are *statistically* predictive. But when used thoughtfully—guided by theory about potential confounders, informed by domain knowledge about likely sparsity patterns, and validated through sensitivity analysis. They offer a middle path between the Scylla of over-specification (including everything) and the Charybdis of under-specification (excluding too much). Used within a joint model of treatment and outcome variable, the argument of a variable selection routine  represents an attempt to calibrate the parameters to select the instrument structure. What variable selection is really doing in joint treatment-outcome models is calibrating the parameters to discover patterns consistent with instrument structure *if such structure exists in the data*. The horseshoe shrinks away coefficients that appear redundant given the covariance structure between treatment, outcome, and covariates. The spike-and-slab actively excludes variables that don't contribute to explaining either margin after accounting for shared variation.\n",
+    " \n",
+    "The ideal use of variable selection in instrumental variable designs is not as a replacement for domain knowledge but as a consistency check. The real power of these methods lies not in automation but in transparency. By making variable selection part of the posterior distribution rather than a pre-processing step, we can quantify and communicate our uncertainty about model structure itself. This moves us closer to the goal of all principled causal inference: not just estimating effects, but understanding the limits of what we can learn from the data we have.\n",
+    " \n",
+    "As always in causal inference, the model is a question posed to the data. Variable selection priors help us ask that question more precisely, but we still need theory to tell us if we're asking the right question at all.\n",
+    "\n"
    ]
   }
  ],

From bf5b40414bb6b51a25aae31df6b47e8c45225c0e Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Wed, 26 Nov 2025 21:44:48 +0000
Subject: [PATCH 16/24] update heading

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 3e293ad5..9f830e89 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2023,7 +2023,7 @@
    "id": "fc265f5d",
    "metadata": {},
    "source": [
-    "## Binary Treatment Case\n",
+    "### Binary Treatment Case\n",
     "\n",
     "Our data generating function output two different simulation scenarios, where the treatment was either continuous or binary. This allows us to demonstrate the joint modelling of the binary treatment outcome which uses a Bernoulli likelihood for the treatment variable and latent confounding to model the joint realisation of treatment and outcome. "
    ]

From b16ef30a24660431ae456675d7a0b9bb494cc6ab Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Wed, 26 Nov 2025 21:51:03 +0000
Subject: [PATCH 17/24] hide cells

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 9f830e89..a3faa0c1 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -586,7 +586,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "763ca253",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1438,7 +1442,9 @@
    "id": "f09b24bf",
    "metadata": {},
    "source": [
-    "#### The Treatment Model"
+    "#### The Treatment Model\n",
+    "\n",
+    "Variable selection is applied to both the outcome and the treatment model. In this way we calibrate our parameters to the joint patterns of realisations between these two endogenous variables. "
    ]
   },
   {
@@ -1706,7 +1712,11 @@
    "cell_type": "code",
    "execution_count": 39,
    "id": "63edfa4e",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2032,7 +2042,11 @@
    "cell_type": "code",
    "execution_count": 44,
    "id": "89e61d28",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",

From b88271eba1621d42d418e246ba755bac2e387ca4 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 12:06:05 +0000
Subject: [PATCH 18/24] better story telling

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 2012 +++++++++++++---------
 1 file changed, 1194 insertions(+), 818 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index a3faa0c1..10c7422f 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -10,7 +10,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pymc_extras/model/marginal/graph_analysis.py:10: FutureWarning: `pytensor.graph.basic.io_toposort` was moved to `pytensor.graph.traversal.io_toposort`. Calling it from the old location will fail in a future release.\n",
+      "  from pytensor.graph.basic import io_toposort\n"
      ]
     }
    ],
@@ -34,9 +35,11 @@
    "source": [
     "## Variable Selection Priors and Instrumental Variable Designs\n",
     "\n",
-    "When building causal inference models, we often face a dilemma: we want to control for confounders to get unbiased causal estimates, but we're not always certain which variables are the true confounders. Include too few, and we risk omitted variable bias. Include too many, and we introduce noise that inflates our uncertainty or, worse, creates multicollinearity that destabilizes our estimates.\n",
+    "When building causal inference models, we face a fundamental dilemma: the bias-variance trade-off. We are tasked with finding the true causal effect of a treatment ($T$) on an outcome ($Y$) while controlling for every single confounder ($X$)—variables that influence both $T$ and $Y$. Include too few variables, and we risk the catastrophic flaw of omitted variable bias (OVB), leading to inaccurate causal claims.Include too many irrelevant variables (noise), and we introduce noise that inflates our uncertainty, widens our posterior intervals, or, worse, creates multicollinearity that destabilizes our estimates.Traditional approaches force us to make hard, upfront decisions about which $X$'s to include. In ideal cases, this is driven by theory. But in the messy reality of data science, where we often have dozens of \"potential\" confounders, how do we make this choice?\n",
     "\n",
-    "Traditional approaches force us to make hard choices upfront—which variables to include, which to exclude. This in ideal cases should be driven by theory. But what if we could let the data help us make these decisions while still maintaining the principled probabilistic framework of Bayesian inference? This is where variable selection priors come in. Let's first simulate some data with some natural confounding structure. "
+    "We want to let the data help us make these decisions while still maintaining the principled probabilistic framework of Bayesian inference. This is where variable selection priors enter the scene. They allow us to encode our uncertainty about variable relevance directly into the prior, making variable selection part of the inference problem itself, not a separate, error-prone preprocessing step.\n",
+    "\n",
+    "Let's first simulate some data with some natural confounding structure and a known true treatment effect of 3.0. We enforce the idea of confounding on the treatment effect with a positive selection probability driving treatment and outcome. The challenge of modelling this data is to tease out the structure of the simultaneous equations in the system. For more details on this Bayesian structural causal modelling [see here](https://causalpy.readthedocs.io/en/latest/knowledgebase/structural_causal_models.html)\n"
    ]
   },
   {
@@ -77,12 +80,12 @@
        "      feature_3\n",
        "      feature_4\n",
        "      ...\n",
-       "      feature_8\n",
-       "      feature_9\n",
-       "      feature_10\n",
-       "      feature_11\n",
-       "      feature_12\n",
-       "      feature_13\n",
+       "      feature_19\n",
+       "      feature_20\n",
+       "      feature_21\n",
+       "      feature_22\n",
+       "      feature_23\n",
+       "      feature_24\n",
        "      Y_cont_scaled\n",
        "      Y_bin_scaled\n",
        "      T_cont_scaled\n",
@@ -92,123 +95,123 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      3.169837\n",
-       "      -0.170346\n",
-       "      1.113394\n",
-       "      0\n",
-       "      9.236102\n",
-       "      1.294441\n",
-       "      0.418241\n",
-       "      0.536286\n",
-       "      -0.615573\n",
-       "      -1.173784\n",
+       "      16.863968\n",
+       "      4.382356\n",
+       "      5.160537\n",
+       "      1\n",
+       "      6.101134\n",
+       "      0.040454\n",
+       "      0.953005\n",
+       "      0.366637\n",
+       "      -0.614435\n",
+       "      0.556259\n",
        "      ...\n",
-       "      0.559393\n",
-       "      1.111766\n",
-       "      -0.216069\n",
-       "      0.451496\n",
-       "      -0.863189\n",
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-       "      0.268475\n",
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-       "      0.347809\n",
-       "      -1.022452\n",
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+       "      1.735675\n",
+       "      0.478207\n",
+       "      1.499834\n",
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+       "      1.676215\n",
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        "    \n",
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-       "      6.662990\n",
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-       "      0.315013\n",
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        "      ...\n",
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        "      ...\n",
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+       "      -1.002202\n",
        "    \n",
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-       "      9.781360\n",
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        "    \n",
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-       "      5.739283\n",
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        "      ...\n",
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@@ -236,183 +239,183 @@
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-       "      1\n",
-       "      5.741192\n",
-       "      -0.103523\n",
-       "      -1.083828\n",
-       "      0.896827\n",
-       "      0.146243\n",
-       "      1.363973\n",
+       "      -2.289532\n",
+       "      0.074541\n",
+       "      -0.788024\n",
+       "      0\n",
+       "      4.426400\n",
+       "      -0.629440\n",
+       "      0.139225\n",
+       "      0.457374\n",
+       "      0.061430\n",
+       "      0.779840\n",
        "      ...\n",
-       "      -1.310958\n",
-       "      -0.004224\n",
-       "      0.206620\n",
-       "      0.012787\n",
-       "      -1.777783\n",
-       "      0.340176\n",
-       "      1.907981\n",
-       "      1.581676\n",
-       "      1.832247\n",
-       "      0.977650\n",
+       "      -1.078842\n",
+       "      1.609967\n",
+       "      -0.110843\n",
+       "      0.326734\n",
+       "      0.428711\n",
+       "      -0.664431\n",
+       "      -0.202534\n",
+       "      -0.351709\n",
+       "      -0.263323\n",
+       "      -1.002202\n",
        "    \n",
        "  \n",
        "\n",
-       "
2500 rows × 23 columns
\n",
+       "
2500 rows × 34 columns
\n",
        ""
       ],
       "text/plain": [
        "         Y_cont     Y_bin    T_cont  T_bin      alpha  feature_0  feature_1  \\\n",
-       "0      3.169837 -0.170346  1.113394      0   9.236102   1.294441   0.418241   \n",
-       "1     10.479049  6.662990  2.272020      1   3.787487  -0.885005   0.315013   \n",
-       "2      7.307821  4.118982  2.062946      1   3.311157  -1.075537   1.058536   \n",
-       "3      9.781360  0.649647  4.043904      1   8.423450   0.969380   0.698199   \n",
-       "4      5.739283  6.812030  0.642417      1   6.613922   0.245569  -0.338491   \n",
+       "0     16.863968  4.382356  5.160537      1   6.101134   0.040454   0.953005   \n",
+       "1    -17.165367 -5.402947 -3.920807      0   7.035316   0.414126  -0.599567   \n",
+       "2     -6.294315 -1.212668 -1.693882      0   2.999955  -1.200018   0.873859   \n",
+       "3      4.273128  2.721006  0.517374      0   6.801450   0.320580  -0.611745   \n",
+       "4    -21.533318 -4.793083 -5.580078      0   6.854237   0.341695  -1.022990   \n",
        "...         ...       ...       ...    ...        ...        ...        ...   \n",
-       "2495  -5.099912 -0.870746 -1.409722      0   6.565630   0.226252   1.589653   \n",
-       "2496 -32.742858 -7.337551 -8.468435      0   4.760520  -0.495792   0.546002   \n",
-       "2497   6.759804  1.912040  1.615921      0  10.445934   1.778373   0.097808   \n",
-       "2498 -11.249395 -1.938808 -3.103529      0   5.715321  -0.113872   0.747480   \n",
-       "2499  21.658258  7.675401  5.660952      1   5.741192  -0.103523  -1.083828   \n",
+       "2495   5.530940 -0.134729  1.888556      0   4.055312  -0.777875   1.191639   \n",
+       "2496   0.191278  4.124587 -1.311103      0  10.253341   1.701336  -1.142284   \n",
+       "2497  14.070809  6.215370  3.618480      1  10.703631   1.881452  -0.973985   \n",
+       "2498   2.888004  4.660163  0.409280      1   5.384630  -0.246148   0.509868   \n",
+       "2499  -2.289532  0.074541 -0.788024      0   4.426400  -0.629440   0.139225   \n",
        "\n",
-       "      feature_2  feature_3  feature_4  ...  feature_8  feature_9  feature_10  \\\n",
-       "0      0.536286  -0.615573  -1.173784  ...   0.559393   1.111766   -0.216069   \n",
-       "1      0.810138   1.137214   0.203685  ...  -0.017179   0.410818    0.670074   \n",
-       "2      1.908944   1.122914   0.611691  ...   0.050641   1.245489   -1.070642   \n",
-       "3      0.314419   1.446987  -2.729092  ...  -1.052429   1.143079   -1.757701   \n",
-       "4      0.814305  -0.859798   0.334968  ...  -0.547622   0.778724    0.678956   \n",
-       "...         ...        ...        ...  ...        ...        ...         ...   \n",
-       "2495   0.056005  -0.386026  -0.462251  ...   0.987633   0.246870   -0.202917   \n",
-       "2496   0.209072   0.666614   0.400847  ...  -0.798775  -0.616483    0.431552   \n",
-       "2497  -0.807658   0.380358  -0.455391  ...   0.668040   1.471963    0.573966   \n",
-       "2498   1.635159  -1.136585  -0.007239  ...  -2.404202  -2.074570    0.022878   \n",
-       "2499   0.896827   0.146243   1.363973  ...  -1.310958  -0.004224    0.206620   \n",
+       "      feature_2  feature_3  feature_4  ...  feature_19  feature_20  \\\n",
+       "0      0.366637  -0.614435   0.556259  ...    0.317673   -0.405886   \n",
+       "1     -0.142998  -0.911652  -1.164335  ...   -0.598014    1.621986   \n",
+       "2     -0.083079  -0.670225  -1.530419  ...   -0.360738   -0.446118   \n",
+       "3     -0.467016   0.358363   0.715248  ...   -1.888617   -2.589217   \n",
+       "4     -0.885820  -2.550334   0.194493  ...    0.345643   -0.634460   \n",
+       "...         ...        ...        ...  ...         ...         ...   \n",
+       "2495  -0.439349   0.313297   0.197013  ...    0.031857   -0.329209   \n",
+       "2496   0.208353  -1.133978   0.289442  ...    0.427981    1.599296   \n",
+       "2497   0.908112  -1.243104   1.122467  ...   -0.893527   -1.874865   \n",
+       "2498  -1.250635   0.822750   0.241686  ...    0.838648   -1.238171   \n",
+       "2499   0.457374   0.061430   0.779840  ...   -1.078842    1.609967   \n",
        "\n",
-       "      feature_11  feature_12  feature_13  Y_cont_scaled  Y_bin_scaled  \\\n",
-       "0       0.451496   -0.863189    0.319180       0.268475     -0.438362   \n",
-       "1       1.954944    0.888255   -0.506518       0.916637      1.321011   \n",
-       "2      -0.060250   -1.857638    0.806913       0.635421      0.666008   \n",
-       "3      -1.167276   -0.164620    1.687004       0.854768     -0.227239   \n",
-       "4       1.715229   -0.439130   -0.238847       0.496327      1.359385   \n",
-       "...          ...         ...         ...            ...           ...   \n",
-       "2495    0.178579    0.763186    0.527462      -0.464865     -0.618693   \n",
-       "2496    1.238957    0.957759   -0.583051      -2.916171     -2.283696   \n",
-       "2497   -0.288768   -0.861025    0.372657       0.586824      0.097788   \n",
-       "2498    1.345018    0.705361    0.414329      -1.010186     -0.893686   \n",
-       "2499    0.012787   -1.777783    0.340176       1.907981      1.581676   \n",
+       "      feature_21  feature_22  feature_23  feature_24  Y_cont_scaled  \\\n",
+       "0       0.448298    2.445401    1.735675    0.478207       1.499834   \n",
+       "1       1.163407    0.215947    0.467359    0.107958      -1.524703   \n",
+       "2      -0.358958   -1.115625   -0.808388    1.389596      -0.558480   \n",
+       "3       0.051731   -0.290556    1.257686    0.346784       0.380757   \n",
+       "4      -0.297935    0.896732   -0.230345    0.966827      -1.912927   \n",
+       "...          ...         ...         ...         ...            ...   \n",
+       "2495   -1.368298   -0.381252    0.477627    0.818295       0.492551   \n",
+       "2496    0.913846    0.528808    0.631644    0.548712       0.017961   \n",
+       "2497   -0.913328    1.194171    0.518187    1.204433       1.251577   \n",
+       "2498    0.611964    0.145091   -0.504683    1.611315       0.257647   \n",
+       "2499   -0.110843    0.326734    0.428711   -0.664431      -0.202534   \n",
        "\n",
-       "      T_cont_scaled  T_bin_scaled  \n",
-       "0          0.347809     -1.022452  \n",
-       "1          0.726014      0.977650  \n",
-       "2          0.657767      0.977650  \n",
-       "3          1.304402      0.977650  \n",
-       "4          0.194070      0.977650  \n",
-       "...             ...           ...  \n",
-       "2495      -0.475800     -1.022452  \n",
-       "2496      -2.779944     -1.022452  \n",
-       "2497       0.511847     -1.022452  \n",
-       "2498      -1.028702     -1.022452  \n",
-       "2499       1.832247      0.977650  \n",
+       "      Y_bin_scaled  T_cont_scaled  T_bin_scaled  \n",
+       "0         0.762745       1.676215      0.997403  \n",
+       "1        -1.768763      -1.284772     -1.002202  \n",
+       "2        -0.684716      -0.558680     -1.002202  \n",
+       "3         0.332945       0.162304     -1.002202  \n",
+       "4        -1.610988      -1.825780     -1.002202  \n",
+       "...            ...            ...           ...  \n",
+       "2495     -0.405848       0.609380     -1.002202  \n",
+       "2496      0.696059      -0.433874     -1.002202  \n",
+       "2497      1.236955       1.173425      0.997403  \n",
+       "2498      0.834615       0.127060      0.997403  \n",
+       "2499     -0.351709      -0.263323     -1.002202  \n",
        "\n",
-       "[2500 rows x 23 columns]"
+       "[2500 rows x 34 columns]"
       ]
      },
      "execution_count": 2,
@@ -426,18 +429,18 @@
     "    return 1 / (1 + np.exp(-z))\n",
     "\n",
     "\n",
-    "def simulate_data(n=2500, alpha_true=3.0, rho=0.6, cate_estimation=False):\n",
+    "def simulate_data(n=2500, alpha_true=3.0, rho=0.6, cate_estimation=False, n_zeros=50):\n",
     "    # Exclusion restrictions:\n",
     "    # X[0], X[1] affect both Y and T (confounders)\n",
     "    # X[2], X[3] affect ONLY T (instruments for T)\n",
     "    # X[4] affects ONLY Y (predictor of Y only)\n",
     "\n",
-    "    betaY = np.array(\n",
-    "        [0.5, -0.3, 0.0, 0.0, 0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-    "    )  # X[2], X[3] excluded\n",
-    "    betaD = np.array(\n",
-    "        [0.7, 0.1, -0.4, 0.3, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-    "    )  # X[4] excluded\n",
+    "    betaY_core = np.array([0.5, -0.3, 0.0, 0.0, 0.4])\n",
+    "    betaD_core = np.array([0.7, 0.1, -0.4, 0.3, 0.0])\n",
+    "\n",
+    "    betaY = np.concatenate([betaY_core, np.zeros(n_zeros)])\n",
+    "    betaD = np.concatenate([betaD_core, np.zeros(n_zeros)])\n",
+    "\n",
     "    p = len(betaY)\n",
     "\n",
     "    # noise variances and correlation\n",
@@ -497,7 +500,7 @@
     "    return data\n",
     "\n",
     "\n",
-    "data = simulate_data()\n",
+    "data = simulate_data(n_zeros=20)\n",
     "instruments_data = data.copy()\n",
     "features = [col for col in data.columns if \"feature\" in col]\n",
     "formula = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n",
@@ -510,11 +513,11 @@
    "id": "e2472e18",
    "metadata": {},
    "source": [
-    "CausalPy's  `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world.\n",
+    "CausalPy's  `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world. For references to a discussion of variable selection priors we direct the reader to {cite:t}`kaplan_bs_social_science`\n",
     "\n",
     "#### The Spike-and-Slab: Discrete Choices\n",
     "\n",
-    "The spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n",
+    "Traditional spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n",
     "\n",
     "$$ \\beta_{j} = \\gamma_{j} \\cdot \\beta_{j_\\text{raw}}$$\n",
     "\n",
@@ -531,7 +534,7 @@
     "\n",
     "$$ \\beta_{j} = \\tau \\cdot \\tilde{\\lambda}_j \\cdot \\beta_{j\\text{raw}}$$\n",
     "\n",
-    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\tilde{\\lambda}_j$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
+    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\tilde{\\lambda}_j$ is local or specific to each coefficient and regularised so as to ensure finite variance. These terms do not have to be zero or one, they reflect a graded weighting. These make it less interpretable than a spike and slab allocation of coefficient weights, but we do not risk pre-maturely zero-ing out weak predictors. \n"
    ]
   },
   {
@@ -539,24 +542,24 @@
    "id": "806df6ea",
    "metadata": {},
    "source": [
-    "### Hyperparameters for Variable Selection Priors\n",
+    "### Spike and Slab: Hyperparameters for Variable Selection Priors\n",
     "\n",
     "You can control the behaviour of the variable selection priors through some of the hyperparameters available. For the spike and slab prior, the most important hyperparamers are `temperature`, `pi_alpha`, and `pi_beta`. \n",
     "\n",
     "Because our sampler doesn't like discrete variables, we're approximating a bernoulli outcome in our sampling to define the spike and slab. The approximation is governed by the `temperature` parameter. The default value of 0.1 works well in most cases, creating indicators that cluster near 0 or 1 without causing sampling difficulties.\n",
     "\n",
-    "The selection probability parameters `pi_alpha` and `pi_beta` encode your prior belief about sparsity. With both set to 2 (the default), you're placing a Beta(2,2) prior on π, the overall proportion of selected variables. This is symmetric around 0.5 but slightly concentrated there—you're saying \"I don't know how many variables are relevant, but probably not all of them and probably not none of them.\""
+    "The selection probability parameters `pi_alpha` and `pi_beta` encode your prior belief about sparsity. With both set to 2 (the default), you're placing a Beta(2,2) prior on π, the overall proportion of selected variables. This is symmetric around 0.5 but slightly concentrated there—you're saying \"I don't know how many variables are relevant, but probably not all of them and probably not none of them.\" These hyper-parameters inform the generation of $\\gamma$ which is pulled toward zero or one depending on the hyperparameters. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 3,
    "id": "ae848fe9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -579,12 +582,12 @@
    "id": "3237bb49",
    "metadata": {},
    "source": [
-    "We'll now fit two models and estimate the implied treatment effect."
+    "We'll now fit two models and estimate the implied treatment effect. We fit the defauly IV model and then the same model that uses spike and slab priors on the input variables."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "763ca253",
    "metadata": {
     "tags": [
@@ -606,70 +609,92 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
-      "                This is not necessarily a problem but it violates\n",
-      "                the assumption of a simple IV experiment.\n",
-      "                The coefficients should be interpreted appropriately.\n",
+      "/Users/nathanielforde/Documents/Github/CausalPy/causalpy/experiments/instrumental_variable.py:204: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                We will use the multivariate normal likelihood\n",
+      "                for continuous treatment.\n",
       "  warnings.warn(\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [beta_t, beta_z, chol_cov]\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b332c2837f1849329b3b561a9765f3e5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 133 seconds.\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Compiling.. :   0%|          | 0/3000 [00:00\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 551 seconds.\n",
-      "There were 167 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Compiling.. :   0%|          | 0/3000 [00:00\n",
        "\n",
-       "\n",
        "\n",
        "\n",
        "\n",
        "\n",
-       "clusterinstruments (15)\n",
+       "clusterinstruments (26)\n",
        "\n",
-       "instruments (15)\n",
+       "instruments (26)\n",
        "\n",
        "\n",
-       "clustercovariates (16)\n",
+       "clustercovariates (27)\n",
        "\n",
-       "covariates (16)\n",
+       "covariates (27)\n",
        "\n",
        "\n",
        "cluster3\n",
@@ -862,7 +911,7 @@
        "Normal\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "mu_t\n",
        "\n",
        "mu_t\n",
@@ -884,7 +933,7 @@
        "Normal\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "mu_y\n",
        "\n",
        "mu_y\n",
@@ -956,7 +1005,7 @@
        "Multivariate_normal\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "chol_cov->likelihood\n",
        "\n",
        "\n",
@@ -969,20 +1018,20 @@
        "~\n",
        "Deterministic\n",
        "\n",
-       "\n",
-       "\n",
-       "mu_y->mu\n",
-       "\n",
-       "\n",
-       "\n",
        "\n",
-       "\n",
+       "\n",
        "mu_t->mu\n",
        "\n",
        "\n",
        "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
        "\n",
-       "\n",
+       "\n",
        "mu->likelihood\n",
        "\n",
        "\n",
@@ -991,10 +1040,10 @@
        "\n"
       ],
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 17,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1008,12 +1057,12 @@
    "id": "34f3a1b7",
    "metadata": {},
    "source": [
-    "Now compare the structure of the spike and slab model. "
+    "Now compare the structure of the spike and slab model. As before we're fitting a joint model of treatment and outcome. But now we specify hierarchical priors over the beta coefficients to \"select\" the variables of real importance. In the IV case this is particularly interesting because it helps mitigate the risk of over-parameterisation with weak instruments. In effect the sparsity priors act like a \"instrument discovery\" mechanism when used in this joint modelling context. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 5,
    "id": "4f8c2685",
    "metadata": {},
    "outputs": [
@@ -1023,302 +1072,302 @@
        "\n",
        "\n",
-       "\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "clusterinstruments (15)\n",
-       "\n",
-       "instruments (15)\n",
+       "clusterinstruments (26)\n",
+       "\n",
+       "instruments (26)\n",
        "\n",
        "\n",
-       "clustercovariates (16)\n",
-       "\n",
-       "covariates (16)\n",
+       "clustercovariates (27)\n",
+       "\n",
+       "covariates (27)\n",
        "\n",
        "\n",
        "cluster3\n",
-       "\n",
-       "3\n",
+       "\n",
+       "3\n",
        "\n",
        "\n",
        "cluster2 x 2\n",
-       "\n",
-       "2 x 2\n",
+       "\n",
+       "2 x 2\n",
        "\n",
        "\n",
        "cluster2\n",
-       "\n",
-       "2\n",
+       "\n",
+       "2\n",
        "\n",
        "\n",
        "cluster2500\n",
-       "\n",
-       "2500\n",
+       "\n",
+       "2500\n",
        "\n",
        "\n",
        "cluster2500 x 2\n",
-       "\n",
-       "2500 x 2\n",
-       "\n",
-       "\n",
-       "\n",
-       "pi_beta_z\n",
-       "\n",
-       "pi_beta_z\n",
-       "~\n",
-       "Beta\n",
-       "\n",
-       "\n",
-       "\n",
-       "gamma_beta_z\n",
-       "\n",
-       "gamma_beta_z\n",
-       "~\n",
-       "Deterministic\n",
-       "\n",
-       "\n",
-       "\n",
-       "pi_beta_z->gamma_beta_z\n",
-       "\n",
-       "\n",
+       "\n",
+       "2500 x 2\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "pi_beta_t\n",
-       "\n",
-       "pi_beta_t\n",
-       "~\n",
-       "Beta\n",
+       "\n",
+       "pi_beta_t\n",
+       "~\n",
+       "Beta\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "gamma_beta_t\n",
-       "\n",
-       "gamma_beta_t\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "gamma_beta_t\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "pi_beta_t->gamma_beta_t\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
-       "\n",
-       "\n",
-       "beta_t_raw\n",
-       "\n",
-       "beta_t_raw\n",
-       "~\n",
-       "Normal\n",
+       "\n",
+       "\n",
+       "pi_beta_z\n",
+       "\n",
+       "pi_beta_z\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z\n",
+       "\n",
+       "gamma_beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z->gamma_beta_z\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n",
        "beta_t\n",
-       "\n",
-       "beta_t\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "beta_t_raw->beta_t\n",
-       "\n",
-       "\n",
+       "gamma_beta_t->beta_t\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "mu_t\n",
-       "\n",
-       "mu_t\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
        "\n",
        "beta_t->mu_t\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "gamma_beta_t->beta_t\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "gamma_beta_t_u\n",
-       "\n",
-       "gamma_beta_t_u\n",
-       "~\n",
-       "Uniform\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "~\n",
+       "Uniform\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "gamma_beta_t_u->gamma_beta_t\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
-       "\n",
-       "\n",
-       "gamma_beta_z_u\n",
-       "\n",
-       "gamma_beta_z_u\n",
-       "~\n",
-       "Uniform\n",
+       "\n",
+       "\n",
+       "beta_t_raw\n",
+       "\n",
+       "beta_t_raw\n",
+       "~\n",
+       "Normal\n",
        "\n",
-       "\n",
-       "\n",
-       "gamma_beta_z_u->gamma_beta_z\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw->beta_t\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "beta_z\n",
-       "\n",
-       "beta_z\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "mu_y\n",
-       "\n",
-       "mu_y\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
        "\n",
        "beta_z->mu_y\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "gamma_beta_z->beta_z\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "beta_z_raw\n",
-       "\n",
-       "beta_z_raw\n",
-       "~\n",
-       "Normal\n",
+       "\n",
+       "beta_z_raw\n",
+       "~\n",
+       "Normal\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "beta_z_raw->beta_z\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z->beta_z\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n",
        "chol_cov\n",
-       "\n",
-       "chol_cov\n",
-       "~\n",
-       "_LKJCholeskyCov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
        "\n",
        "\n",
        "\n",
        "cov\n",
-       "\n",
-       "cov\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
        "\n",
        "chol_cov->cov\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n",
        "chol_cov_corr\n",
-       "\n",
-       "chol_cov_corr\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
        "\n",
        "chol_cov->chol_cov_corr\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n",
        "chol_cov_stds\n",
-       "\n",
-       "chol_cov_stds\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
        "\n",
        "\n",
        "chol_cov->chol_cov_stds\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n",
        "likelihood\n",
-       "\n",
-       "likelihood\n",
-       "~\n",
-       "Multivariate_normal\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "chol_cov->likelihood\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n",
        "mu\n",
-       "\n",
-       "mu\n",
-       "~\n",
-       "Deterministic\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "mu_y->mu\n",
-       "\n",
-       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
        "\n",
-       "\n",
+       "\n",
        "\n",
-       "mu_t->mu\n",
-       "\n",
-       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
-       "\n",
+       "\n",
        "mu->likelihood\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
        "\n",
        "\n",
        "\n"
       ],
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 18,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1337,13 +1386,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "id": "0755095c",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "
"
       ]
@@ -1366,13 +1415,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "id": "838e0726",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]
@@ -1411,15 +1460,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "id": "127888b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -1437,6 +1486,14 @@
     "axs[0].set_title(\"Parameter Comparison Outcome Model \\n Baseline v Spike and Slab\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2d6e01dd",
+   "metadata": {},
+   "source": [
+    "The parameter estimates are healthy for both models. With the spike and slab being a little bit more agressive. But differences are more pronounced in the cull of the instruments. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f09b24bf",
@@ -1444,20 +1501,22 @@
    "source": [
     "#### The Treatment Model\n",
     "\n",
-    "Variable selection is applied to both the outcome and the treatment model. In this way we calibrate our parameters to the joint patterns of realisations between these two endogenous variables. "
+    "Variable selection is applied to both the outcome and the treatment model. In this way we calibrate our parameters to the joint patterns of realisations between these two endogenous variables. The Normal model (with all covariates included) is the baseline, showing the impact of including many irrelevant variables. Its posterior is centered correctly but is generally wider, reflecting the increased uncertainty/variance due to the noise covariates.\n",
+    "\n",
+    "The Spike-and-Slab prior performs better here. This suggests it was successful in decisively excluding the noise variables, reducing the model's overall variance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "id": "acafc928",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -1486,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "f2a0b213",
    "metadata": {},
    "outputs": [
@@ -1519,99 +1578,165 @@
        "  \n",
        "    
\n",
        "      0\n",
-       "      0.01750\n",
+       "      0.02250\n",
        "      False\n",
-       "      0.020546\n",
+       "      0.027836\n",
        "    
\n",
        "    
\n",
        "      1\n",
        "      1.00000\n",
        "      True\n",
-       "      0.991285\n",
+       "      0.987589\n",
        "    
\n",
        "    
\n",
        "      2\n",
-       "      0.64100\n",
+       "      0.66675\n",
        "      True\n",
-       "      0.659357\n",
+       "      0.690207\n",
        "    
\n",
        "    
\n",
        "      3\n",
-       "      0.58200\n",
+       "      0.64050\n",
        "      True\n",
-       "      0.616580\n",
+       "      0.674015\n",
        "    
\n",
        "    
\n",
        "      4\n",
-       "      0.17625\n",
+       "      0.03800\n",
        "      False\n",
-       "      0.192949\n",
+       "      0.044785\n",
        "    
\n",
        "    
\n",
        "      5\n",
-       "      0.06075\n",
+       "      0.05700\n",
        "      False\n",
-       "      0.068297\n",
+       "      0.065583\n",
        "    
\n",
        "    
\n",
        "      6\n",
-       "      0.66600\n",
+       "      0.65600\n",
        "      True\n",
-       "      0.702671\n",
+       "      0.692559\n",
        "    
\n",
        "    
\n",
        "      7\n",
-       "      0.01000\n",
+       "      0.01325\n",
        "      False\n",
-       "      0.012679\n",
+       "      0.015218\n",
        "    
\n",
        "    
\n",
        "      8\n",
-       "      0.01300\n",
+       "      0.00525\n",
        "      False\n",
-       "      0.016342\n",
+       "      0.006931\n",
        "    
\n",
        "    
\n",
        "      9\n",
-       "      0.00975\n",
+       "      0.00200\n",
        "      False\n",
-       "      0.012392\n",
+       "      0.003972\n",
        "    
\n",
        "    
\n",
        "      10\n",
-       "      0.01700\n",
+       "      0.00400\n",
        "      False\n",
-       "      0.019508\n",
+       "      0.005936\n",
        "    
\n",
        "    
\n",
        "      11\n",
-       "      0.01625\n",
+       "      0.00550\n",
        "      False\n",
-       "      0.021217\n",
+       "      0.007275\n",
        "    
\n",
        "    
\n",
        "      12\n",
-       "      0.02000\n",
+       "      0.00525\n",
        "      False\n",
-       "      0.024469\n",
+       "      0.006648\n",
        "    
\n",
        "    
\n",
        "      13\n",
-       "      0.01825\n",
+       "      0.00700\n",
        "      False\n",
-       "      0.023474\n",
+       "      0.008973\n",
        "    
\n",
        "    
\n",
        "      14\n",
-       "      0.02700\n",
+       "      0.01325\n",
        "      False\n",
-       "      0.031636\n",
+       "      0.015868\n",
        "    
\n",
        "    
\n",
        "      15\n",
-       "      0.01650\n",
+       "      0.00625\n",
+       "      False\n",
+       "      0.007979\n",
+       "    
\n",
+       "    
\n",
+       "      16\n",
+       "      0.06150\n",
+       "      False\n",
+       "      0.072126\n",
+       "    
\n",
+       "    
\n",
+       "      17\n",
+       "      0.01000\n",
+       "      False\n",
+       "      0.013383\n",
+       "    
\n",
+       "    
\n",
+       "      18\n",
+       "      0.00675\n",
+       "      False\n",
+       "      0.008561\n",
+       "    
\n",
+       "    
\n",
+       "      19\n",
+       "      0.00650\n",
        "      False\n",
-       "      0.020863\n",
+       "      0.008700\n",
+       "    
\n",
+       "    
\n",
+       "      20\n",
+       "      0.00675\n",
+       "      False\n",
+       "      0.009016\n",
+       "    
\n",
+       "    
\n",
+       "      21\n",
+       "      0.01050\n",
+       "      False\n",
+       "      0.012205\n",
+       "    
\n",
+       "    
\n",
+       "      22\n",
+       "      0.01875\n",
+       "      False\n",
+       "      0.022215\n",
+       "    
\n",
+       "    
\n",
+       "      23\n",
+       "      0.00650\n",
+       "      False\n",
+       "      0.008177\n",
+       "    
\n",
+       "    
\n",
+       "      24\n",
+       "      0.00900\n",
+       "      False\n",
+       "      0.011439\n",
+       "    
\n",
+       "    
\n",
+       "      25\n",
+       "      0.00650\n",
+       "      False\n",
+       "      0.008250\n",
+       "    
\n",
+       "    
\n",
+       "      26\n",
+       "      0.00575\n",
+       "      False\n",
+       "      0.007574\n",
        "    
\n",
        "  \n",
        "\n",
@@ -1619,25 +1744,36 @@
       ],
       "text/plain": [
        "       prob  selected  gamma_mean\n",
-       "0   0.01750     False    0.020546\n",
-       "1   1.00000      True    0.991285\n",
-       "2   0.64100      True    0.659357\n",
-       "3   0.58200      True    0.616580\n",
-       "4   0.17625     False    0.192949\n",
-       "5   0.06075     False    0.068297\n",
-       "6   0.66600      True    0.702671\n",
-       "7   0.01000     False    0.012679\n",
-       "8   0.01300     False    0.016342\n",
-       "9   0.00975     False    0.012392\n",
-       "10  0.01700     False    0.019508\n",
-       "11  0.01625     False    0.021217\n",
-       "12  0.02000     False    0.024469\n",
-       "13  0.01825     False    0.023474\n",
-       "14  0.02700     False    0.031636\n",
-       "15  0.01650     False    0.020863"
+       "0   0.02250     False    0.027836\n",
+       "1   1.00000      True    0.987589\n",
+       "2   0.66675      True    0.690207\n",
+       "3   0.64050      True    0.674015\n",
+       "4   0.03800     False    0.044785\n",
+       "5   0.05700     False    0.065583\n",
+       "6   0.65600      True    0.692559\n",
+       "7   0.01325     False    0.015218\n",
+       "8   0.00525     False    0.006931\n",
+       "9   0.00200     False    0.003972\n",
+       "10  0.00400     False    0.005936\n",
+       "11  0.00550     False    0.007275\n",
+       "12  0.00525     False    0.006648\n",
+       "13  0.00700     False    0.008973\n",
+       "14  0.01325     False    0.015868\n",
+       "15  0.00625     False    0.007979\n",
+       "16  0.06150     False    0.072126\n",
+       "17  0.01000     False    0.013383\n",
+       "18  0.00675     False    0.008561\n",
+       "19  0.00650     False    0.008700\n",
+       "20  0.00675     False    0.009016\n",
+       "21  0.01050     False    0.012205\n",
+       "22  0.01875     False    0.022215\n",
+       "23  0.00650     False    0.008177\n",
+       "24  0.00900     False    0.011439\n",
+       "25  0.00650     False    0.008250\n",
+       "26  0.00575     False    0.007574"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1656,7 +1792,11 @@
    "source": [
     "### Horseshoe\n",
     "\n",
-    "The horseshoe prior is a sophisticated continuous shrinkage method designed for regularization and variable selection in high-dimensional regression settings. Unlike discrete selection approaches, it operates through a elegant hierarchical structure that adaptively shrinks coefficients based on the strength of their signal in the data. The key to the implementation is the hierarchical $\\lambda$ component: \n",
+    "The horsehoe prior takes a continuous philosophical stance. Instead of discrete selection, it says: effects exist on a spectrum from negligible to substantial, and we should shrink them proportionally to their signal strength.\n",
+    "\n",
+    "It uses a hierarchical structure where the coefficient $\\beta_j$ is determined by a global shrinkage parameter ($\\tau$) and a local shrinkage parameter so that effects get pulled strongly toward zero (but rarely exactly zero) but there is a chance for counterfailing effects on each individual variable. Large effects escape shrinkage almost entirely, as if they are too big for the horseshoe's magnetic pull.This method embraces probabilistic humility. It's less decisive than the spike-and-slab, but it mitigates the risk of completely zeroing out the small, but real, contributions of certain variables — a risk that the spike-and-slab takes.\n",
+    "\n",
+    "The key to the implementation is the hierarchical $\\lambda$ component: \n",
     "\n",
     "$$ \\tilde{\\lambda}_j = \\sqrt{\\frac{c^2 \\lambda_j^2}{c^2 + \\tau^2 \\lambda_j^2}} $$\n",
     "\n",
@@ -1669,13 +1809,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 11,
    "id": "16bb5f90",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]
@@ -1710,7 +1850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 12,
    "id": "63edfa4e",
    "metadata": {
     "tags": [
@@ -1732,66 +1872,89 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
-      "                This is not necessarily a problem but it violates\n",
-      "                the assumption of a simple IV experiment.\n",
-      "                The coefficients should be interpreted appropriately.\n",
+      "/Users/nathanielforde/Documents/Github/CausalPy/causalpy/experiments/instrumental_variable.py:204: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                We will use the multivariate normal likelihood\n",
+      "                for continuous treatment.\n",
       "  warnings.warn(\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "/Users/nathanielforde/Documents/Github/CausalPy/causalpy/pymc_models.py:723: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
       "  warnings.warn(\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [tau_beta_t, lambda_beta_t, c2_beta_t, beta_t_raw, tau_beta_z, lambda_beta_z, c2_beta_z, beta_z_raw, chol_cov]\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1410b52c995348e397aed1a2843f2bb7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 534 seconds.\n",
-      "There were 16 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Compiling.. :   0%|          | 0/3000 [00:00\n",
        "    \n",
        "      0\n",
-       "      0.014780\n",
-       "      1.222524\n",
-       "      0.036811\n",
+       "      0.043874\n",
+       "      2.328468\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      0.697739\n",
-       "      57.713961\n",
-       "      0.036811\n",
+       "      2.245092\n",
+       "      119.151445\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.160282\n",
-       "      13.257798\n",
-       "      0.036811\n",
+       "      0.680149\n",
+       "      36.096825\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.129649\n",
-       "      10.724023\n",
-       "      0.036811\n",
+       "      0.602132\n",
+       "      31.956327\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.076676\n",
-       "      6.342298\n",
-       "      0.036811\n",
+       "      0.080390\n",
+       "      4.266463\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      5\n",
-       "      0.025650\n",
-       "      2.121680\n",
-       "      0.036811\n",
+       "      0.106834\n",
+       "      5.669885\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      6\n",
-       "      0.190186\n",
-       "      15.731398\n",
-       "      0.036811\n",
+       "      0.649063\n",
+       "      34.447035\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      7\n",
-       "      0.014198\n",
-       "      1.174382\n",
-       "      0.036811\n",
+       "      0.029781\n",
+       "      1.580511\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      8\n",
-       "      0.014636\n",
-       "      1.210634\n",
-       "      0.036811\n",
+       "      0.028220\n",
+       "      1.497674\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      9\n",
-       "      0.014024\n",
-       "      1.160002\n",
-       "      0.036811\n",
+       "      0.028227\n",
+       "      1.498050\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      10\n",
-       "      0.014441\n",
-       "      1.194485\n",
-       "      0.036811\n",
+       "      0.027964\n",
+       "      1.484083\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      11\n",
-       "      0.018907\n",
-       "      1.563934\n",
-       "      0.036811\n",
+       "      0.026990\n",
+       "      1.432398\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      12\n",
-       "      0.018518\n",
-       "      1.531734\n",
-       "      0.036811\n",
+       "      0.027155\n",
+       "      1.441149\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      13\n",
-       "      0.017415\n",
-       "      1.440514\n",
-       "      0.036811\n",
+       "      0.027431\n",
+       "      1.455806\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      14\n",
-       "      0.019238\n",
-       "      1.591291\n",
-       "      0.036811\n",
+       "      0.031783\n",
+       "      1.686802\n",
+       "      0.016754\n",
        "    \n",
        "    \n",
        "      15\n",
-       "      0.017481\n",
-       "      1.445946\n",
-       "      0.036811\n",
+       "      0.028214\n",
+       "      1.497361\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      16\n",
+       "      0.065193\n",
+       "      3.459899\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      17\n",
+       "      0.035303\n",
+       "      1.873624\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      18\n",
+       "      0.028928\n",
+       "      1.535271\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      19\n",
+       "      0.031675\n",
+       "      1.681079\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      20\n",
+       "      0.031461\n",
+       "      1.669710\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      21\n",
+       "      0.031398\n",
+       "      1.666369\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      22\n",
+       "      0.042932\n",
+       "      2.278498\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      23\n",
+       "      0.030349\n",
+       "      1.610704\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      24\n",
+       "      0.034713\n",
+       "      1.842312\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      25\n",
+       "      0.027761\n",
+       "      1.473313\n",
+       "      0.016754\n",
+       "    \n",
+       "    \n",
+       "      26\n",
+       "      0.028744\n",
+       "      1.525479\n",
+       "      0.016754\n",
        "    \n",
        "  \n",
        "\n",
@@ -1958,25 +2187,36 @@
       ],
       "text/plain": [
        "    shrinkage_factor  lambda_tilde       tau\n",
-       "0           0.014780      1.222524  0.036811\n",
-       "1           0.697739     57.713961  0.036811\n",
-       "2           0.160282     13.257798  0.036811\n",
-       "3           0.129649     10.724023  0.036811\n",
-       "4           0.076676      6.342298  0.036811\n",
-       "5           0.025650      2.121680  0.036811\n",
-       "6           0.190186     15.731398  0.036811\n",
-       "7           0.014198      1.174382  0.036811\n",
-       "8           0.014636      1.210634  0.036811\n",
-       "9           0.014024      1.160002  0.036811\n",
-       "10          0.014441      1.194485  0.036811\n",
-       "11          0.018907      1.563934  0.036811\n",
-       "12          0.018518      1.531734  0.036811\n",
-       "13          0.017415      1.440514  0.036811\n",
-       "14          0.019238      1.591291  0.036811\n",
-       "15          0.017481      1.445946  0.036811"
+       "0           0.043874      2.328468  0.016754\n",
+       "1           2.245092    119.151445  0.016754\n",
+       "2           0.680149     36.096825  0.016754\n",
+       "3           0.602132     31.956327  0.016754\n",
+       "4           0.080390      4.266463  0.016754\n",
+       "5           0.106834      5.669885  0.016754\n",
+       "6           0.649063     34.447035  0.016754\n",
+       "7           0.029781      1.580511  0.016754\n",
+       "8           0.028220      1.497674  0.016754\n",
+       "9           0.028227      1.498050  0.016754\n",
+       "10          0.027964      1.484083  0.016754\n",
+       "11          0.026990      1.432398  0.016754\n",
+       "12          0.027155      1.441149  0.016754\n",
+       "13          0.027431      1.455806  0.016754\n",
+       "14          0.031783      1.686802  0.016754\n",
+       "15          0.028214      1.497361  0.016754\n",
+       "16          0.065193      3.459899  0.016754\n",
+       "17          0.035303      1.873624  0.016754\n",
+       "18          0.028928      1.535271  0.016754\n",
+       "19          0.031675      1.681079  0.016754\n",
+       "20          0.031461      1.669710  0.016754\n",
+       "21          0.031398      1.666369  0.016754\n",
+       "22          0.042932      2.278498  0.016754\n",
+       "23          0.030349      1.610704  0.016754\n",
+       "24          0.034713      1.842312  0.016754\n",
+       "25          0.027761      1.473313  0.016754\n",
+       "26          0.028744      1.525479  0.016754"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1990,13 +2230,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 14,
    "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]
@@ -2040,7 +2280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 17,
    "id": "89e61d28",
    "metadata": {
     "tags": [
@@ -2062,44 +2302,157 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [beta_t, beta_z, sigma_U, rho_unconstr, eps_raw]\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
-      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c7276bf767824813a312c4c3ccff7c01",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 68 seconds.\n",
+      "Compiling.. :   0%|          | 0/3000 [00:00"
       ]
@@ -2148,7 +2513,10 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(20, 6))\n",
     "az.plot_posterior(\n",
-    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    "    result_horseshoe_binary.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_bin\"]},\n",
+    "    ax=ax,\n",
     ")\n",
     "az.plot_posterior(\n",
     "    result_normal_binary.idata,\n",
@@ -2160,6 +2528,14 @@
     "ax.axvline(3, color=\"black\", linestyle=\"--\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "95027a9c",
+   "metadata": {},
+   "source": [
+    "In the Binary case the Horsehoe prior"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "36ad018b",
@@ -2176,7 +2552,7 @@
     " \n",
     "In our simulations, both approaches identified the true treatment effect of 3, though they arrived there differently. The spike-and-slab showed more confidence, producing tighter posterior intervals by decisively excluding noise variables. The horseshoe's bi-modal posterior in some specifications revealed its uncertainty about the appropriate level of sparsity a kind of probabilistic humility that spike-and-slab's discrete choices don't allow.\n",
     "\n",
-    "**Practical Guidance:**\n",
+    "#### Practical Guidance\n",
     " \n",
     "- **Use spike-and-slab when** you have strong priors about sparsity (many potential confounders, few true ones), when interpretability matters (stakeholders want to know \"what's included?\"), or when you're willing to trade some flexibility for more decisive inference.\n",
     " \n",
@@ -2184,7 +2560,7 @@
     " \n",
     "- **Use neither when** theory clearly identifies your confounders, when sample size is large relative to the number of predictors, or when the cost of Type I errors (including irrelevant variables) is low relative to Type II errors (excluding true confounders).\n",
     " \n",
-    "**Final Thoughts:**\n",
+    "#### Final Thoughts\n",
     " \n",
     "Variable selection priors don't eliminate the need for causal reasoning. They don't tell you which variables are *causally* relevant, only which are *statistically* predictive. But when used thoughtfully—guided by theory about potential confounders, informed by domain knowledge about likely sparsity patterns, and validated through sensitivity analysis. They offer a middle path between the Scylla of over-specification (including everything) and the Charybdis of under-specification (excluding too much). Used within a joint model of treatment and outcome variable, the argument of a variable selection routine  represents an attempt to calibrate the parameters to select the instrument structure. What variable selection is really doing in joint treatment-outcome models is calibrating the parameters to discover patterns consistent with instrument structure *if such structure exists in the data*. The horseshoe shrinks away coefficients that appear redundant given the covariance structure between treatment, outcome, and covariates. The spike-and-slab actively excludes variables that don't contribute to explaining either margin after accounting for shared variation.\n",
     " \n",
@@ -2211,7 +2587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.8"
+   "version": "3.13.11"
   }
  },
  "nbformat": 4,

From c452650fbf1ddce3a8ee43394a01ecefb3aec19d Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 12:11:23 +0000
Subject: [PATCH 19/24] tidying

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 10c7422f..60ab7f35 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2533,7 +2533,7 @@
    "id": "95027a9c",
    "metadata": {},
    "source": [
-    "In the Binary case the Horsehoe prior"
+    "In the Binary case the biases due to weak instruments are overcome by both modelling strategies. "
    ]
   },
   {

From a5dd60fd2fbf641747df2c1b3535324fb0c0fd75 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 12:14:41 +0000
Subject: [PATCH 20/24] hide cell inputs

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 60ab7f35..789d8db1 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -555,7 +555,11 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "ae848fe9",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -1811,7 +1815,11 @@
    "cell_type": "code",
    "execution_count": 11,
    "id": "16bb5f90",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [
     {
      "data": {

From a7c1090b43e8caf55a8f462a951cb1601b579326 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 12:33:00 +0000
Subject: [PATCH 21/24] fixing linting

Signed-off-by: Nathaniel 
---
 causalpy/pymc_models.py                   |  5 ++--
 causalpy/variable_selection_priors.py     | 32 +++++++++++------------
 docs/source/_static/interrogate_badge.svg |  8 +++---
 3 files changed, 23 insertions(+), 22 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index d0a0aee6..4f9ba54f 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -677,7 +677,7 @@ def build_model(  # type: ignore
         Z: np.ndarray,
         y: np.ndarray,
         t: np.ndarray,
-        coords: Dict[str, Any],
+        coords: dict[str, Any],
         priors,
         vs_prior_type=None,
         vs_hyperparams=None,
@@ -721,7 +721,8 @@ def build_model(  # type: ignore
                 warnings.warn(
                     "Variable selection priors specified. "
                     "The 'mus' and 'sigmas' in the priors dict will be ignored "
-                    "for beta coefficients. Only 'eta' and 'lkj_sd' will be used."
+                    "for beta coefficients. Only 'eta' and 'lkj_sd' will be used.",
+                    stacklevel=2,
                 )
 
             # Create coefficient priors
diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index 8c8b2001..50812085 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -20,7 +20,7 @@
 top of the pymc-extras Prior infrastructure.
 """
 
-from typing import Any, Dict, Optional, Union
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -30,7 +30,7 @@
 
 
 def _relaxed_bernoulli_transform(
-    p: Union[float, pt.TensorVariable], temperature: float = 0.1
+    p: float | pt.TensorVariable, temperature: float = 0.1
 ):
     """
     Transform function for relaxed (continuous) Bernoulli distribution.
@@ -100,7 +100,7 @@ def __init__(
         pi_beta: float = 2,
         slab_sigma: float = 2,
         temperature: float = 0.1,
-        dims: Optional[Union[str, tuple]] = None,
+        dims: str | tuple | None = None,
     ):
         self.pi_alpha = pi_alpha
         self.pi_beta = pi_beta
@@ -176,11 +176,11 @@ class HorseshoePrior:
 
     def __init__(
         self,
-        tau0: Optional[float] = None,
+        tau0: float | None = None,
         nu: float = 3,
         c2_alpha: float = 2,
         c2_beta: float = 2,
-        dims: Optional[Union[str, tuple]] = None,
+        dims: str | tuple | None = None,
     ):
         self.tau0 = tau0
         self.nu = nu
@@ -277,7 +277,7 @@ class VariableSelectionPrior:
     ...     beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
     """
 
-    def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None):
+    def __init__(self, prior_type: str, hyperparams: dict[str, Any] | None = None):
         """Initialize the variable selection prior factory."""
         self.prior_type = prior_type.lower()
         self.hyperparams = hyperparams or {}
@@ -292,8 +292,8 @@ def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None
         self._prior_instance = None
 
     def _get_default_hyperparams(
-        self, n_params: int, X: Optional[np.ndarray] = None
-    ) -> Dict[str, Any]:
+        self, n_params: int, X: np.ndarray | None = None
+    ) -> dict[str, Any]:
         """
         Get default hyperparameters for the chosen prior type.
 
@@ -346,10 +346,10 @@ def create_prior(
         self,
         name: str,
         n_params: int,
-        dims: Optional[Union[str, tuple]] = None,
-        X: Optional[np.ndarray] = None,
-        hyperparams: Optional[Dict[str, Any]] = None,
-    ) -> Union[pm.Deterministic, pm.Distribution]:
+        dims: str | tuple | None = None,
+        X: np.ndarray | None = None,
+        hyperparams: dict[str, Any] | None = None,
+    ) -> pm.Deterministic | pm.Distribution:
         """
         Create the specified prior on a coefficient vector.
 
@@ -559,10 +559,10 @@ def create_variable_selection_prior(
     prior_type: str,
     name: str,
     n_params: int,
-    dims: Optional[Union[str, tuple]] = None,
-    X: Optional[np.ndarray] = None,
-    hyperparams: Optional[Dict[str, Any]] = None,
-) -> Union[pm.Deterministic, pm.Distribution]:
+    dims: str | tuple | None = None,
+    X: np.ndarray | None = None,
+    hyperparams: dict[str, Any] | None = None,
+) -> pm.Deterministic | pm.Distribution:
     """
     Convenience function to create a variable selection prior in one call.
 
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index 4ed4f3af..d2d886ad 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,10 +1,10 @@
 
-    interrogate: 94.6%
+    interrogate: 95.4%
     
         
             
         
-        
+        
         
             
         
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        94.6%
-        94.6%
+        95.4%
+        95.4%
     
     
         

From 78ed0ce21081b988291cad2a3f4d907865ab0404 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 12:46:26 +0000
Subject: [PATCH 22/24] fix linting error

Signed-off-by: Nathaniel 
---
 causalpy/experiments/instrumental_variable.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py
index d603b60f..93403579 100644
--- a/causalpy/experiments/instrumental_variable.py
+++ b/causalpy/experiments/instrumental_variable.py
@@ -204,7 +204,8 @@ def input_validation(self) -> None:
             warnings.warn(
                 """Warning. The treatment variable is not Binary.
                 We will use the multivariate normal likelihood
-                for continuous treatment."""
+                for continuous treatment.""",
+                stacklevel=2,
             )
 
     def get_2SLS_fit(self) -> None:

From 9e6ede03c859d59a9bb9ebca58bea4c58f587740 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 16:16:33 +0000
Subject: [PATCH 23/24] update typing

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index 50812085..e484b7fb 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -349,7 +349,7 @@ def create_prior(
         dims: str | tuple | None = None,
         X: np.ndarray | None = None,
         hyperparams: dict[str, Any] | None = None,
-    ) -> pm.Deterministic | pm.Distribution:
+    ) -> pm.Deterministic:
         """
         Create the specified prior on a coefficient vector.
 
@@ -562,7 +562,7 @@ def create_variable_selection_prior(
     dims: str | tuple | None = None,
     X: np.ndarray | None = None,
     hyperparams: dict[str, Any] | None = None,
-) -> pm.Deterministic | pm.Distribution:
+) -> pm.Deterministic:
     """
     Convenience function to create a variable selection prior in one call.
 

From dbc8614e5713487491bbf14b40444344c93ca17f Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Tue, 9 Dec 2025 20:45:16 +0000
Subject: [PATCH 24/24] spell check

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 789d8db1..f01fb219 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -586,7 +586,7 @@
    "id": "3237bb49",
    "metadata": {},
    "source": [
-    "We'll now fit two models and estimate the implied treatment effect. We fit the defauly IV model and then the same model that uses spike and slab priors on the input variables."
+    "We'll now fit two models and estimate the implied treatment effect. We fit the default IV model and then the same model that uses spike and slab priors on the input variables."
    ]
   },
   {
@@ -1495,7 +1495,7 @@
    "id": "2d6e01dd",
    "metadata": {},
    "source": [
-    "The parameter estimates are healthy for both models. With the spike and slab being a little bit more agressive. But differences are more pronounced in the cull of the instruments. "
+    "The parameter estimates are healthy for both models. With the spike and slab being a little bit more aggressive. But differences are more pronounced in the cull of the instruments. "
    ]
   },
   {