pymc.DiscreteWeibull#

class pymc.DiscreteWeibull(name, *args, **kwargs)[source]#

Discrete Weibull log-likelihood

The discrete Weibull distribution is a flexible model of count data that can handle both over- and under-dispersion. The pmf of this distribution is

\[f(x \mid q, \beta) = q^{x^{\beta}} - q^{(x + 1)^{\beta}}\]

(Source code, png, hires.png, pdf)

../../../_images/pymc-DiscreteWeibull-1.png

Support

\(x \in \mathbb{N}_0\)

Mean

\(\mu = \sum_{x = 1}^{\infty} q^{x^{\beta}}\)

Variance

\(2 \sum_{x = 1}^{\infty} x q^{x^{\beta}} - \mu - \mu^2\)

Methods

DiscreteWeibull.__init__(*args, **kwargs)

DiscreteWeibull.dist(q, beta, *args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

DiscreteWeibull.logcdf(q, beta)

Compute the log of the cumulative distribution function for Discrete Weibull distribution at the specified value.

DiscreteWeibull.logp(q, beta)

Calculate log-probability of DiscreteWeibull distribution at specified value.

DiscreteWeibull.moment(size, q, beta)

Attributes

rv_class

rv_op