JointPDF — Stochastic Variables and Information Theory in Python

This python module implements the notion of a “joint probability distribution” among discrete stochastic variables, as well as many common operations for them. Secondly, it implements many information-theoretical quantities such as Shannon entropy, mutual information, information synergy, information-based optimization procedures, and robustness tests.