Pathfinder model specification fix

examples/introductory/api_quickstart.myst.md

@@ -87,7 +87,7 @@ Every probabilistic program consists of observed and unobserved Random Variables
 - {ref}`pymc:api_distributions_discrete`
 - {ref}`pymc:api_distributions_multivariate`
 - {ref}`pymc:api_distributions_mixture`
-- {ref}`pymc:api_distributions_rimeseries`
+- {ref}`pymc:api_distributions_timeseries`
 - {ref}`pymc:api_distributions_censored`
 - {ref}`pymc:api_distributions_simulator`
 

examples/variational_inference/pathfinder.myst.md

@@ -38,6 +38,7 @@ Instructions for installing other packages:
 
 ```{code-cell} ipython3
 import arviz as az
+import matplotlib.pyplot as plt
 import numpy as np
 import pymc as pm
 import pymc_experimental as pmx
@@ -57,22 +58,23 @@ with pm.Model() as model:
     mu = pm.Normal("mu", mu=0.0, sigma=10.0)
     tau = pm.HalfCauchy("tau", 5.0)
 
-    theta = pm.Normal("theta", mu=0, sigma=1, shape=J)
-    theta_1 = mu + tau * theta
+    z = pm.Normal("z", mu=0, sigma=1, shape=J)
+    theta = mu + tau * z
     obs = pm.Normal("obs", mu=theta, sigma=sigma, shape=J, observed=y)
 ```
 
 Next, we call `pmx.fit()` and pass in the algorithm we want it to use.
 
 ```{code-cell} ipython3
 with model:
-    idata = pmx.fit(method="pathfinder")
+    idata = pmx.fit(method="pathfinder", num_samples=1000)
 ```
 
 Just like `pymc.sample()`, this returns an idata with samples from the posterior. Note that because these samples do not come from an MCMC chain, convergence can not be assessed in the regular way.
 
 ```{code-cell} ipython3
-az.plot_trace(idata);
+az.plot_trace(idata)
+plt.tight_layout();
 ```
 
 ## References
@@ -86,7 +88,8 @@ az.plot_trace(idata);
 ## Authors
 
 * Authored by Thomas Wiecki on Oct 11 2022 ([pymc-examples#429](https://github.com/pymc-devs/pymc-examples/pull/429))
-* Re-execute notebook, by Reshama Shaikh on Feb 5, 2023
+* Re-execute notebook by Reshama Shaikh on Feb 5, 2023
+* Bug fix by Chris Fonnesbeck on Jul 17, 2024
 
 +++