Lea 3.3.0 is now released!

http://pypi.org/project/lea/3.3.0

What is Lea?
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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?
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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...
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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,

Pierre Denis

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