From 4b730b8753c7e03a9fed051d34914e53e613e173 Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Wed, 13 Nov 2024 23:05:05 +0530
Subject: [PATCH 1/7] Update multivariate.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality, as the class provides more than just log-likelihood, including cdf, mean, and random methods.
---
 pymc/distributions/multivariate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index da10b12fa..9a927e7be 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -194,7 +194,7 @@ def quaddist_chol(value, mu, cov):
 
 class MvNormal(Continuous):
     r"""
-    Multivariate normal log-likelihood.
+    Multivariate normal distribution.
 
     .. math::
 

From eaa22ae25bd46ba12fc9443d104aeeacc143d486 Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Wed, 27 Nov 2024 17:43:43 +0530
Subject: [PATCH 2/7] Update multivariate.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality, as the class provides more than just log-likelihood, including mean, variance, covariance.
---
 pymc/distributions/multivariate.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index 9a927e7be..80dce87de 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -394,7 +394,7 @@ def rng_fn(cls, rng, nu, mu, cov, size):
 
 class MvStudentT(Continuous):
     r"""
-    Multivariate Student-T log-likelihood.
+    Multivariate Student-T distribution.
 
     .. math::
         f(\mathbf{x}| \nu,\mu,\Sigma) =
@@ -491,7 +491,7 @@ def logp(value, nu, mu, scale):
 
 class Dirichlet(SimplexContinuous):
     r"""
-    Dirichlet log-likelihood.
+    Dirichlet distribution.
 
     .. math::
 
@@ -563,7 +563,7 @@ def logp(value, a):
 
 class Multinomial(Discrete):
     r"""
-    Multinomial log-likelihood.
+    Multinomial distribution.
 
     Generalizes binomial distribution, but instead of each trial resulting
     in "success" or "failure", each one results in exactly one of some
@@ -691,7 +691,7 @@ def rv_op(cls, n, a, *, size=None, rng=None):
 
 
 class DirichletMultinomial(Discrete):
-    r"""Dirichlet Multinomial log-likelihood.
+    r"""Dirichlet Multinomial distribution.
 
     Dirichlet mixture of Multinomials distribution, with a marginalized PMF.
 
@@ -950,7 +950,7 @@ def rng_fn(cls, rng, nu, V, size):
 
 class Wishart(Continuous):
     r"""
-    Wishart log-likelihood.
+    Wishart distribution.
 
     The Wishart distribution is the probability distribution of the
     maximum-likelihood estimator (MLE) of the precision matrix of a
@@ -1648,7 +1648,7 @@ def lkjcorr_default_transform(op, rv):
 
 class LKJCorr:
     r"""
-    The LKJ (Lewandowski, Kurowicka and Joe) log-likelihood.
+    The LKJ (Lewandowski, Kurowicka and Joe) distribution.
 
     The LKJ distribution is a prior distribution for correlation matrices.
     If eta = 1 this corresponds to the uniform distribution over correlation
@@ -1753,7 +1753,7 @@ def rng_fn(cls, rng, mu, rowchol, colchol, size=None):
 
 class MatrixNormal(Continuous):
     r"""
-    Matrix-valued normal log-likelihood.
+    Matrix-valued normal distribution.
 
     .. math::
        f(x \mid \mu, U, V) =
@@ -1961,7 +1961,7 @@ def rv_op(cls, mu, sigma, *covs, size=None, rng=None):
 
 class KroneckerNormal(Continuous):
     r"""
-    Multivariate normal log-likelihood with Kronecker-structured covariance.
+    Multivariate normal distribution with Kronecker-structured covariance.
 
     .. math::
 

From 40001d25b7fce249141d19c7100b73c19608c2fb Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Wed, 27 Nov 2024 21:42:01 +0530
Subject: [PATCH 3/7] Update continuous.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality, as the class provides more than just log-likelihood, including mean, variance, covariance, etc.
---
 pymc/distributions/continuous.py | 60 ++++++++++++++++----------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index 803418528..e8a0cccd9 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -246,7 +246,7 @@ def get_tau_sigma(
 
 class Uniform(BoundedContinuous):
     r"""
-    Continuous uniform log-likelihood.
+    Continuous uniform distribution.
 
     The pdf of this distribution is
 
@@ -360,7 +360,7 @@ def rng_fn(cls, rng, size):
 
 
 class Flat(Continuous):
-    """Uninformative log-likelihood that returns 0 regardless of the passed value."""
+    """Uninformative distribution that returns 0 regardless of the passed value."""
 
     rv_op = flat
 
@@ -417,7 +417,7 @@ def logcdf(value):
 
 class Normal(Continuous):
     r"""
-    Univariate normal log-likelihood.
+    Univariate normal distribution.
 
     The pdf of this distribution is
 
@@ -558,7 +558,7 @@ def rng_fn(
 
 class TruncatedNormal(BoundedContinuous):
     r"""
-    Univariate truncated normal log-likelihood.
+    Univariate truncated normal distribution.
 
     The pdf of this distribution is
 
@@ -745,7 +745,7 @@ def truncated_normal_default_transform(op, rv):
 
 class HalfNormal(PositiveContinuous):
     r"""
-    Half-normal log-likelihood.
+    Half-normal distribution.
 
     The pdf of this distribution is
 
@@ -875,7 +875,7 @@ def rng_fn(cls, rng, mu, lam, alpha, size) -> np.ndarray:
 
 class Wald(PositiveContinuous):
     r"""
-    Wald log-likelihood.
+    Wald distribution.
 
     The pdf of this distribution is
 
@@ -1055,7 +1055,7 @@ def rng_fn(cls, rng, alpha, beta, size) -> np.ndarray:
 
 class Beta(UnitContinuous):
     r"""
-    Beta log-likelihood.
+    Beta distribution.
 
     The pdf of this distribution is
 
@@ -1241,7 +1241,7 @@ def rv_op(cls, a, b, *, size=None, rng=None):
 
 class Kumaraswamy(UnitContinuous):
     r"""
-    Kumaraswamy log-likelihood.
+    Kumaraswamy distribution.
 
     The pdf of this distribution is
 
@@ -1331,7 +1331,7 @@ def logcdf(value, a, b):
 
 class Exponential(PositiveContinuous):
     r"""
-    Exponential log-likelihood.
+    Exponential distribution.
 
     The pdf of this distribution is
 
@@ -1426,7 +1426,7 @@ def icdf(value, mu):
 
 class Laplace(Continuous):
     r"""
-    Laplace log-likelihood.
+    Laplace distribution.
 
     The pdf of this distribution is
 
@@ -1548,7 +1548,7 @@ def rv_op(cls, b, kappa, mu, *, size=None, rng=None):
 
 class AsymmetricLaplace(Continuous):
     r"""
-    Asymmetric-Laplace log-likelihood.
+    Asymmetric-Laplace distribution.
 
     The pdf of this distribution is
 
@@ -1639,7 +1639,7 @@ def logp(value, b, kappa, mu):
 
 class LogNormal(PositiveContinuous):
     r"""
-    Log-normal log-likelihood.
+    Log-normal distribution.
 
     Distribution of any random variable whose logarithm is normally
     distributed. A variable might be modeled as log-normal if it can
@@ -1758,7 +1758,7 @@ def icdf(value, mu, sigma):
 
 class StudentT(Continuous):
     r"""
-    Student's T log-likelihood.
+    Student's T distribution.
 
     Describes a normal variable whose precision is gamma distributed.
     If only nu parameter is passed, this specifies a standard (central)
@@ -1904,7 +1904,7 @@ def rng_fn(cls, rng, a, b, mu, sigma, size=None) -> np.ndarray:
 
 class SkewStudentT(Continuous):
     r"""
-    Skewed Student's T distribution log-likelihood.
+    Skewed Student's T distribution distribution.
 
     This follows Jones and Faddy (2003)
 
@@ -2019,7 +2019,7 @@ def icdf(value, a, b, mu, sigma):
 
 class Pareto(BoundedContinuous):
     r"""
-    Pareto log-likelihood.
+    Pareto distribution.
 
     Often used to characterize wealth distribution, or other examples of the
     80/20 rule.
@@ -2128,7 +2128,7 @@ def pareto_default_transform(op, rv):
 
 class Cauchy(Continuous):
     r"""
-    Cauchy log-likelihood.
+    Cauchy distribution.
 
     Also known as the Lorentz or the Breit-Wigner distribution.
 
@@ -2216,7 +2216,7 @@ def icdf(value, alpha, beta):
 
 class HalfCauchy(PositiveContinuous):
     r"""
-    Half-Cauchy log-likelihood.
+    Half-Cauchy distribution.
 
     The pdf of this distribution is
 
@@ -2300,7 +2300,7 @@ def icdf(value, loc, beta):
 
 class Gamma(PositiveContinuous):
     r"""
-    Gamma log-likelihood.
+    Gamma distribution.
 
     Represents the sum of alpha exponentially distributed random variables,
     each of which has rate beta.
@@ -2429,7 +2429,7 @@ def icdf(value, alpha, scale):
 
 class InverseGamma(PositiveContinuous):
     r"""
-    Inverse gamma log-likelihood, the reciprocal of the gamma distribution.
+    Inverse gamma distribution, the reciprocal of the gamma distribution.
 
     The pdf of this distribution is
 
@@ -2545,7 +2545,7 @@ def logcdf(value, alpha, beta):
 
 class ChiSquared:
     r"""
-    :math:`\chi^2` log-likelihood.
+    :math:`\chi^2` distribution.
 
     This is the distribution from the sum of the squares of :math:`\nu` independent standard normal random variables or a special
     case of the gamma distribution with :math:`\alpha = \nu/2` and :math:`\beta = 1/2`.
@@ -2617,7 +2617,7 @@ def rng_fn(cls, rng, alpha, beta, size) -> np.ndarray:
 
 class Weibull(PositiveContinuous):
     r"""
-    Weibull log-likelihood.
+    Weibull distribution.
 
     The pdf of this distribution is
 
@@ -2738,7 +2738,7 @@ def rv_op(cls, nu, sigma, *, size=None, rng=None) -> np.ndarray:
 
 class HalfStudentT(PositiveContinuous):
     r"""
-    Half Student's T log-likelihood.
+    Half Student's T distribution.
 
     The pdf of this distribution is
 
@@ -2859,7 +2859,7 @@ def rv_op(cls, mu, sigma, nu, *, size=None, rng=None):
 
 class ExGaussian(Continuous):
     r"""
-    Exponentially modified Gaussian log-likelihood.
+    Exponentially modified Gaussian distribution.
 
     Results from the convolution of a normal distribution with an exponential
     distribution.
@@ -2982,7 +2982,7 @@ def logcdf(value, mu, sigma, nu):
 
 class VonMises(CircularContinuous):
     r"""
-    Univariate VonMises log-likelihood.
+    Univariate VonMises distribution.
 
     The pdf of this distribution is
 
@@ -3068,7 +3068,7 @@ def rng_fn(cls, rng, mu, sigma, alpha, size=None) -> np.ndarray:
 
 class SkewNormal(Continuous):
     r"""
-    Univariate skew-normal log-likelihood.
+    Univariate skew-normal distribution.
 
     The pdf of this distribution is
 
@@ -3163,7 +3163,7 @@ def logp(value, mu, sigma, alpha):
 
 class Triangular(BoundedContinuous):
     r"""
-    Continuous Triangular log-likelihood.
+    Continuous Triangular distribution.
 
     The pdf of this distribution is
 
@@ -3292,7 +3292,7 @@ def triangular_default_transform(op, rv):
 
 class Gumbel(Continuous):
     r"""
-    Univariate right-skewed Gumbel log-likelihood.
+    Univariate right-skewed Gumbel distribution.
 
     This distribution is typically used for modeling maximum (or extreme) values.
     Those looking to find the extreme minimum provided by the left-skewed Gumbel should
@@ -3519,7 +3519,7 @@ def logp(value, b, sigma):
 
 class Logistic(Continuous):
     r"""
-    Logistic log-likelihood.
+    Logistic distribution.
 
     The pdf of this distribution is
 
@@ -3627,7 +3627,7 @@ def rv_op(cls, mu, sigma, *, size=None, rng=None):
 
 class LogitNormal(UnitContinuous):
     r"""
-    Logit-Normal log-likelihood.
+    Logit-Normal distribution.
 
     The pdf of this distribution is
 
@@ -3872,7 +3872,7 @@ def rng_fn(cls, rng, mu, sigma, size=None) -> np.ndarray:
 
 class Moyal(Continuous):
     r"""
-    Moyal log-likelihood.
+    Moyal distribution.
 
     The pdf of this distribution is
 

From 77cab6e2ba9f4e1360f2fd86aa37a989fe6a6218 Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Wed, 27 Nov 2024 21:46:54 +0530
Subject: [PATCH 4/7] Update discrete.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality, as the class provides more than just log-likelihood, including mean, variance, covariance.
---
 pymc/distributions/discrete.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/pymc/distributions/discrete.py b/pymc/distributions/discrete.py
index 179bae25f..b0d23a99a 100644
--- a/pymc/distributions/discrete.py
+++ b/pymc/distributions/discrete.py
@@ -71,7 +71,7 @@
 
 class Binomial(Discrete):
     R"""
-    Binomial log-likelihood.
+    Binomial distribution.
 
     The discrete probability distribution of the number of successes
     in a sequence of n independent yes/no experiments, each of which
@@ -176,7 +176,7 @@ def logcdf(value, n, p):
 
 class BetaBinomial(Discrete):
     R"""
-    Beta-binomial log-likelihood.
+    Beta-binomial distribution.
 
     Equivalent to binomial random variable with success probability
     drawn from a beta distribution.
@@ -293,7 +293,7 @@ def logcdf(value, n, alpha, beta):
 
 
 class Bernoulli(Discrete):
-    R"""Bernoulli log-likelihood.
+    R"""Bernoulli distribution.
 
     The Bernoulli distribution describes the probability of successes
     (x=1) and failures (x=0).
@@ -413,7 +413,7 @@ def rv_op(cls, q, beta, *, size=None, rng=None):
 
 
 class DiscreteWeibull(Discrete):
-    R"""Discrete Weibull log-likelihood.
+    R"""Discrete Weibull distribution.
 
     The discrete Weibull distribution is a flexible model of count data that
     can handle both over- and under-dispersion.
@@ -506,7 +506,7 @@ def logcdf(value, q, beta):
 
 class Poisson(Discrete):
     R"""
-    Poisson log-likelihood.
+    Poisson distribution.
 
     Often used to model the number of events occurring in a fixed period
     of time when the times at which events occur are independent.
@@ -602,7 +602,7 @@ def logcdf(value, mu):
 
 class NegativeBinomial(Discrete):
     R"""
-    Negative binomial log-likelihood.
+    Negative binomial distribution.
 
     The negative binomial distribution describes a Poisson random variable
     whose rate parameter is gamma distributed.
@@ -750,7 +750,7 @@ def logcdf(value, n, p):
 
 class Geometric(Discrete):
     R"""
-    Geometric log-likelihood.
+    Geometric distribution.
 
     The probability that the first success in a sequence of Bernoulli
     trials occurs on the x'th trial.
@@ -1084,7 +1084,7 @@ def icdf(value, lower, upper):
 
 class Categorical(Discrete):
     R"""
-    Categorical log-likelihood.
+    Categorical distribution.
 
     The most general discrete distribution. The pmf of this distribution is
 

From ce38886863d9769bd9a628fa459eaf22a38556f3 Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Wed, 27 Nov 2024 21:49:37 +0530
Subject: [PATCH 5/7] Update distribution.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality, as the class provides more than just log-likelihood including log-cdf, log-pdf.
---
 pymc/distributions/distribution.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymc/distributions/distribution.py b/pymc/distributions/distribution.py
index 8e55f649d..a76857227 100644
--- a/pymc/distributions/distribution.py
+++ b/pymc/distributions/distribution.py
@@ -668,7 +668,7 @@ def rv_op(cls, c, *, size=None, rng=None):
 
 class DiracDelta(Discrete):
     r"""
-    DiracDelta log-likelihood.
+    DiracDelta distribution.
 
     Parameters
     ----------

From d24ee39d9762a20695ee26467e038e9dcaaa5519 Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Thu, 28 Nov 2024 17:53:59 +0530
Subject: [PATCH 6/7] Update mixture.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality, as the class provides more than just log-likelihood, including mean, variance and covariance.
---
 pymc/distributions/mixture.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/pymc/distributions/mixture.py b/pymc/distributions/mixture.py
index dc704e512..125dc3c1e 100644
--- a/pymc/distributions/mixture.py
+++ b/pymc/distributions/mixture.py
@@ -57,7 +57,7 @@
 
 
 class MarginalMixtureRV(SymbolicRandomVariable):
-    """A placeholder used to specify a log-likelihood for a mixture sub-graph."""
+    """A placeholder used to specify a distribution for a mixture sub-graph."""
 
     _print_name = ("MarginalMixture", "\\operatorname{MarginalMixture}")
 
@@ -163,7 +163,7 @@ def update(self, node: Apply):
 
 class Mixture(Distribution):
     R"""
-    Mixture log-likelihood.
+    Mixture distribution.
 
     Often used to model subpopulation heterogeneity
 
@@ -493,7 +493,7 @@ def mixture_args_fn(rng, weights, *components):
 
 class NormalMixture:
     R"""
-    Normal mixture log-likelihood.
+    Normal mixture distribution.
 
     .. math::
 
@@ -573,7 +573,7 @@ def _zero_inflated_mixture(*, name, nonzero_p, nonzero_dist, **kwargs):
 
 class ZeroInflatedPoisson:
     R"""
-    Zero-inflated Poisson log-likelihood.
+    Zero-inflated Poisson distribution.
 
     Often used to model the number of events occurring in a fixed period
     of time when the times at which events occur are independent.
@@ -637,7 +637,7 @@ def dist(cls, psi, mu, **kwargs):
 
 class ZeroInflatedBinomial:
     R"""
-    Zero-inflated Binomial log-likelihood.
+    Zero-inflated Binomial distribution.
 
     The pmf of this distribution is
 
@@ -701,7 +701,7 @@ def dist(cls, psi, n, p, **kwargs):
 
 class ZeroInflatedNegativeBinomial:
     R"""
-    Zero-Inflated Negative binomial log-likelihood.
+    Zero-Inflated Negative binomial distribution.
 
     The Zero-inflated version of the Negative Binomial (NB).
     The NB distribution describes a Poisson random variable
@@ -831,7 +831,7 @@ def _hurdle_mixture(*, name, nonzero_p, nonzero_dist, dtype, max_n_steps=10_000,
 
 class HurdlePoisson:
     R"""
-    Hurdle Poisson log-likelihood.
+    Hurdle Poisson distribution.
 
     The Poisson distribution is often used to model the number of events occurring
     in a fixed period of time or space when the times or locations
@@ -877,7 +877,7 @@ def dist(cls, psi, mu, **kwargs):
 
 class HurdleNegativeBinomial:
     R"""
-    Hurdle Negative Binomial log-likelihood.
+    Hurdle Negative Binomial distribution.
 
     The negative binomial distribution describes a Poisson random variable
     whose rate parameter is gamma distributed.
@@ -935,7 +935,7 @@ def dist(cls, psi, mu=None, alpha=None, p=None, n=None, **kwargs):
 
 class HurdleGamma:
     R"""
-    Hurdle Gamma log-likelihood.
+    Hurdle Gamma distribution.
 
     .. math::
 
@@ -987,7 +987,7 @@ def dist(cls, psi, alpha=None, beta=None, mu=None, sigma=None, **kwargs):
 
 class HurdleLogNormal:
     R"""
-    Hurdle LogNormal log-likelihood.
+    Hurdle LogNormal distribution.
 
     .. math::
 

From e79f4315137b963c20b236c00f95d83a545e710e Mon Sep 17 00:00:00 2001
From: Tanish Yelgoe <143334319+tanishy7777@users.noreply.github.com>
Date: Thu, 28 Nov 2024 17:56:29 +0530
Subject: [PATCH 7/7] Update timeseries.py

Updated the docstrings for better clarity. Replaced "log-likelihood" with "distribution" to accurately describe the functionality.
---
 pymc/distributions/timeseries.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/distributions/timeseries.py b/pymc/distributions/timeseries.py
index 6469cd101..e9095cd0d 100644
--- a/pymc/distributions/timeseries.py
+++ b/pymc/distributions/timeseries.py
@@ -419,7 +419,7 @@ def get_dists(
 
 
 class AutoRegressiveRV(SymbolicRandomVariable):
-    """A placeholder used to specify a log-likelihood for an AR sub-graph."""
+    """A placeholder used to specify a distribution for an AR sub-graph."""
 
     extended_signature = "(o),(),(o),(s),[rng]->[rng],(t)"
     ar_order: int
@@ -865,7 +865,7 @@ def garch11_support_point(op, rv, omega, alpha_1, beta_1, initial_vol, init_dist
 
 
 class EulerMaruyamaRV(SymbolicRandomVariable):
-    """A placeholder used to specify a log-likelihood for a EulerMaruyama sub-graph."""
+    """A placeholder used to specify a distribution for a EulerMaruyama sub-graph."""
 
     dt: float
     sde_fn: Callable