Lea 3.4.0 is now released! ---> http://pypi.org/project/lea/3.4.0
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 and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
Lea can be used for AI, education (probability theory & PP), generation of random samples, etc. LGPL - Python 2.6+ / Python 3 supported
For a 5 minutes tour. check out the poster presented at PROBPROG2020 conference: http://probprog.cc/assets/posters/fri/69.pdf
What's new in Lea 3.4.0? ------------------------ Lea 3.4.0 includes two important improvements over 3.3.x: 1) Introduction of "evidence contexts", allowing to factorize conditions when calculating conditional probabilities
http://bitbucket.org/piedenis/lea/wiki/Lea3_Tutorial_3#markdown-header-evide nce-contexts-evidence-add_evidence-methods 2) Optimize calculation for several queries, which were intractable in an exact way with previous Lea versions
This version contains also a couple of improvements on usability/consistency.
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 erratic,