The module contains a Python implementation of functions related to the Poisson Binomial probability distribution [1], which describes the probability distribution of the sum of independent Bernoulli random variables with non-uniform success probabilities. For further information, see reference [1].

The implemented methods are:

• pmf: probability mass function
• cdf: cumulative distribution function
• pval: p-value for right tailed tests

Mika Straka

The newest version can be found on https://github.com/tsakim/poibin

• NumPy
• pytest For testing

Consider n independent and non-identically distributed random variables and be p a list/NumPy array of the corresponding Bernoulli success probabilities. In order to create the Poisson Binomial distributions, use

from poibin import PoiBin
pb = PoiBin(p)

Be x a list/NumPy array of different numbers of success. Use the following methods to obtain the corresponding quantities:

• Probability mass function

pb.pmf(x)

• Cumulative distribution function

pb.cdf(x)

• P-values for right tailed tests

pb.pval(x)

All three methods accept single integers as well as lists/NumPy arrays of integers. Note that x[i] must be smaller than len(p).

The methods have been implemented using the pytest module. To run the tests, execute

$ pytest test_poibin.py

in the command line. For verbose mode, use

$ pytest -v test_poibin.py

Yili Hong, On computing the distribution function for the Poisson binomial distribution, Computational Statistics & Data Analysis, Volume 59, March 2013, pages 41-51, ISSN 0167-9473

Copyright (c) 2016-2017 Mika J. Straka