Lea 3.1.0 final is now released! ---> http://pypi.org/project/lea/3.1.0 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, Bayesian networks, joint probability distributions, Markov chains and symbolic computation. Lea can be used for AI, PP, gambling, education, ... LGPL - Python 2.6+ / Python 3 supported What's new in Lea 3.1.0? ------------------------ The present version essentially consolidates the previous one (3.0.1), adding just few new features. The main changes are: - new switch_func method, for defining CPT by a function (far less memory-consuming for models like noisy-or, noisy-max) - improvements on Markov chain functions, including calculation of absorbing MC - optimization of lea.max_of, lea.min_of functions - bug fixes for symbolic calculation (in particular for Python 2.7) - numerous addings/improvements on wiki tutorials and documentation, especially on Lea API Many thanks... -------------- ... to Paul Moore, Jens Finkhaeuser and Rasmus Bonnevie who provided proposals, contributions, feedbacks that gave the impetus for the present version. To learn more... ---------------- Lea 3 on PyPI -> http://pypi.org/project/lea/3.1.0 Lea project page -> http://bitbucket.org/piedenis/lea Documentation -> http://bitbucket.org/piedenis/lea/wiki/Home Statues algorithm -> http://arxiv.org/abs/1806.09997 With the hope that Lea can make this Universe less hazardous, Pierre Denis