What is Lea?
Lea is a Python module aiming at working with discrete probability
distributions in an intuitive way.
It allows you modeling a broad range of random phenomena: gambling, weather,
finance, etc. More generally, Lea may be used for any finite set of discrete
values having known probability: numbers, booleans, date/times, symbols, ...
Each probability distribution is modeled as a plain object, which can be
named, displayed, queried or processed to produce new probability
distributions. Lea also provides advanced functions, machine learning and
Probabilistic Programming (PP) features; these include conditional
probabilities, Bayesian networks, joint probability distributions, Markov
chains, EM algorithm and symbolic computation. Lea can be used for AI, PP,
gambling, education, etc.
LGPL - Python 2.6+ / Python 3 supported
What's new in Lea 3.2.0?
The version 3.2.0 extends Lea with machine learning features. This includes
the Expectation-Maximization algorithm (EM). It allows you to learn
parameters of probabilistic models having hidden variables.