Lea 3.3.0 is now released! ---> http://pypi.org/project/lea/3.3.0
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 3.3.0? ------------------------ The version 3.3.0 extends Lea with advanced Monte-Carlo algorithms, for dealing with problems intractable for exact resolution. These are described in a dedicated page of the wiki: http://bitbucket.org/piedenis/lea/wiki/Lea3_Tutorial_3#markdown-header-appro ximate-inference-by-monte-carlo-approaches This version contains also a couple of improvements on usability.
To learn more... ---------------- Lea 3 on PyPI -> http://pypi.org/project/lea Lea project page -> http://bitbucket.org/piedenis/lea Documentation -> http://bitbucket.org/piedenis/lea/wiki/Home Statues algorithm -> http://link.springer.com/chapter/10.1007/978-3-030-52246-9_10
With the hope that Lea can make this Universe less infectious,