pymc.Poisson#

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

Poisson log-likelihood.

Often used to model the number of events occurring in a fixed period of time when the times at which events occur are independent. The pmf of this distribution is

\[f(x \mid \mu) = \frac{e^{-\mu}\mu^x}{x!}\]

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

../../../_images/pymc-Poisson-1.png

Support

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

Mean

\(\mu\)

Variance

\(\mu\)

Parameters
mutensor_like of float

Expected number of occurrences during the given interval (mu >= 0).

Notes

The Poisson distribution can be derived as a limiting case of the binomial distribution.

Methods

Poisson.__init__(*args, **kwargs)

Poisson.dist(mu, *args, **kwargs)

Creates a tensor variable corresponding to the cls distribution.

Poisson.logcdf(mu)

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

Poisson.logp(mu)

Calculate log-probability of Poisson distribution at specified value.

Poisson.moment(size, mu)

Attributes

rv_class

rv_op