Update docstrings

pymc3/distributions/continuous.py

@@ -4300,6 +4300,7 @@ def __call__(self, h=1, z=0, size=None, **kwargs):
     def rng_fn(cls, rng, h, z, size=None):
         """
         Generate a random sample from the distribution with the given parameters
+
         Parameters
         ----------
         h : scalar or sequence, optional
@@ -4325,18 +4326,25 @@ def rng_fn(cls, rng, h, z, size=None):
 class PolyaGamma(PositiveContinuous):
     r"""
     The Polya-Gamma distribution.
+
     The distribution is parametrized by ``h`` (shape parameter) and ``z``
     (exponential tilting parameter). The pdf of this distribution is
+
     .. math::
+
        f(x \mid h, z) = cosh^h(\frac{z}{2})e^{-\frac{1}{2}xz^2}f(x \mid h, 0),
     where :math:`f(x \mid h, 0)` is the pdf of a :math:`PG(h, 0)` variable.
     Notice that the pdf of this distribution is expressed as an alternating-sign
     sum of inverse-Gaussian densities.
+
     .. math::
+
         X = \Sigma_{k=1}^{\infty}\frac{Ga(h, 1)}{d_k},
     where :math:`d_k = 2(k - 0.5)^2\pi^2 + z^2/2`, :math:`Ga(h, 1)` is a gamma
     random variable with shape  parameter ``h`` and scale parameter ``1``.
+
     .. plot::
+
         import matplotlib.pyplot as plt
         import numpy as np
         from polyagamma import polyagamma_pdf
@@ -4351,25 +4359,30 @@ class PolyaGamma(PositiveContinuous):
         plt.ylabel('f(x)', fontsize=12)
         plt.legend(loc=1)
         plt.show()
+
     ========  =============================
     Support   :math:`x \in (0, \infty)`
     Mean      :math:`dfrac{h}{4} if :math:`z=0`, :math:`\dfrac{tanh(z/2)h}{2z}` otherwise.
     Variance  :math:`0.041666688h` if :math:`z=0`, :math:`\dfrac{h(sinh(z) - z)(1 - tanh^2(z/2))}{4z^3}` otherwise.
     ========  =============================
+
     Parameters
     ----------
     h: float, optional
         The shape parameter of the distribution (h > 0).
     z: float, optional
         The exponential tilting parameter of the distribution.
+
     Examples
     --------
     .. code-block:: python
+
         rng = np.random.default_rng()
         with pm.Model():
             x = pm.PolyaGamma('x', h=1, z=5.5)
         with pm.Model():
             x = pm.PolyaGamma('x', h=25, z=-2.3, rng=rng, size=(100, 5))
+
     References
     ----------
     .. [1] Polson, Nicholas G., James G. Scott, and Jesse Windle.
@@ -4401,28 +4414,32 @@ def dist(cls, h=1.0, z=0.0, rng=None, size=None, **kwargs):
     def logp(value, h, z):
         """
         Calculate log-probability of Normal distribution at specified value.
+
         Parameters
         ----------
         value: numeric
             Value(s) for which log-probability is calculated. If the log probabilities for multiple
             values are desired the values must be provided in a numpy array.
+
         Returns
         -------
         TensorVariable
         """
-        return bound(polyagamma_logpdf(value, h, z), h > 0)
+        return bound(polyagamma_logpdf(value, h, z), h > 0, value > 0)
 
     def logcdf(value, h, z):
         """
         Compute the log of the cumulative distribution function for Normal distribution
         at the specified value.
+
         Parameters
         ----------
         value: numeric or np.ndarray or `TensorVariable`
             Value(s) for which log CDF is calculated. If the log CDF for multiple
             values are desired the values must be provided in a numpy array.
+
         Returns
         -------
         TensorVariable
         """
-        return bound(polyagamma_logcdf(value, h, z), h > 0)
+        return bound(polyagamma_logcdf(value, h, z), h > 0, value > 0)