class pymc.HurdlePoisson(name, psi, mu, **kwargs)[source]#

Hurdle Poisson log-likelihood.

The Poisson distribution is often used to model the number of events occurring in a fixed period of time or space when the times or locations at which events occur are independent.

The difference with ZeroInflatedPoisson is that the zeros are not inflated, they come from a completely independent process.

The pmf of this distribution is

\[\begin{split}f(x \mid \psi, \mu) = \left\{ \begin{array}{l} (1 - \psi) \ \text{if } x = 0 \\ \psi \frac{\text{PoissonPDF}(x \mid \mu))} {1 - \text{PoissonCDF}(0 \mid \mu)} \ \text{if } x=1,2,3,\ldots \end{array} \right.\end{split}\]
psitensor_like of float

Expected proportion of Poisson variates (0 < psi < 1)

mutensor_like of float

Expected number of occurrences (mu >= 0).


HurdlePoisson.dist(psi, mu, **kwargs)