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 MonteCarlo 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#markdownheaderappro ximateinferencebymontecarloapproaches 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/10.1007/9783030522469_10
With the hope that Lea can make this Universe less infectious,
Pierre Denis
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pie.denis＠skynet.be