# 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

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