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Updated pymc.DiscreteWeibull docstring #6283

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merged 2 commits into from
Nov 10, 2022
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Updated pymc.DiscreteWeibull docstring #6283

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7552bb1
Updated pymc.DiscreteWeibull docstring
hyosubkim Nov 9, 2022
5967d15
address review comments
hyosubkim Nov 10, 2022
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11 changes: 10 additions & 1 deletion pymc/distributions/discrete.py
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Expand Up @@ -462,7 +462,7 @@ def rng_fn(cls, rng, q, beta, size):


class DiscreteWeibull(Discrete):
R"""Discrete Weibull log-likelihood
R"""Discrete Weibull log-likelihood.

The discrete Weibull distribution is a flexible model of count data that
can handle both over- and under-dispersion.
Expand All @@ -471,6 +471,7 @@ class DiscreteWeibull(Discrete):
.. math:: f(x \mid q, \beta) = q^{x^{\beta}} - q^{(x + 1)^{\beta}}

.. plot::
:context: close-figs

import matplotlib.pyplot as plt
import numpy as np
Expand Down Expand Up @@ -499,6 +500,14 @@ def DiscreteWeibull(q, b, x):
Mean :math:`\mu = \sum_{x = 1}^{\infty} q^{x^{\beta}}`
Variance :math:`2 \sum_{x = 1}^{\infty} x q^{x^{\beta}} - \mu - \mu^2`
======== ======================

Parameters
----------
q : tensor_like of float
Shape parameter (0 < q < 1).
beta : tensor_like of float
Shape parameter (beta > 0).

"""
rv_op = discrete_weibull

Expand Down