Standardize docstrings of input dists arguments and add warning about cloning

Author: ricardoV94

pymc/distributions/censored.py

@@ -42,9 +42,12 @@ class Censored(SymbolicDistribution):
 
     Parameters
     ----------
-    dist: PyMC unnamed distribution
-        PyMC distribution created via the `.dist()` API, which will be censored. This
-        distribution must be univariate and have a logcdf method implemented.
+    dist: unnamed distribution
+        Univariate distribution created via the `.dist()` API, which will be censored.
+        This distribution must have a logcdf method implemented for sampling.
+
+        .. warning:: dist will be cloned, rendering it independent of the one passed as input.
+
     lower: float or None
         Lower (left) censoring point. If `None` the distribution will not be left censored
     upper: float or None

pymc/distributions/mixture.py

@@ -66,12 +66,15 @@ class Mixture(SymbolicDistribution):
     w : tensor_like of float
         w >= 0 and w <= 1
         the mixture weights
-    comp_dists : iterable of PyMC distributions or single batched distribution
-        Distributions should be created via the `.dist()` API. If single distribution is
-        passed, the last size dimension (not shape) determines the number of mixture
+    comp_dists : iterable of unnamed distributions or single batched distribution
+        Distributions should be created via the `.dist()` API. If a single distribution
+        is passed, the last size dimension (not shape) determines the number of mixture
         components (e.g. `pm.Poisson.dist(..., size=components)`)
         :math:`f_1, \ldots, f_n`
 
+        .. warning:: comp_dists will be cloned, rendering them independent of the ones passed as input.
+
+
     Examples
     --------
     .. code-block:: python

pymc/distributions/multivariate.py

@@ -1271,10 +1271,13 @@ class LKJCholeskyCov:
         larger values put more weight on matrices with few correlations.
     n: int
         Dimension of the covariance matrix (n > 1).
-    sd_dist: pm.Distribution
+    sd_dist: unnamed distribution
         A positive scalar or vector distribution for the standard deviations, created
         with the `.dist()` API. Should have `shape[-1]=n`. Scalar distributions will be
         automatically resized to ensure this.
+
+        .. warning:: sd_dist will be cloned, rendering it independent of the one passed as input.
+
     compute_corr: bool, default=True
         If `True`, returns three values: the Cholesky decomposition, the correlations
         and the standard deviations of the covariance matrix. Otherwise, only returns

pymc/distributions/timeseries.py

@@ -147,9 +147,12 @@ class GaussianRandomWalk(distribution.Continuous):
         innovation drift, defaults to 0.0
     sigma : tensor_like of float, optional
         sigma > 0, innovation standard deviation, defaults to 1.0
-    init : Univariate PyMC distribution
+    init : unnamed distribution
         Univariate distribution of the initial value, created with the `.dist()` API.
         Defaults to Normal with same `mu` and `sigma` as the GaussianRandomWalk
+
+        .. warning:: init will be cloned, rendering them independent of the ones passed as input.
+
     steps : int
         Number of steps in Gaussian Random Walks (steps > 0).
     """