Lea 2.3 is now released! ---> http://pypi.python.org/pypi/lea/2.3.4 What is Lea? ------------ Lea is a Python package aiming at working with discrete probability distributions in an intuitive way. It allows you to model a broad range of random phenomenons, like dice throwing, coin tossing, gambling, finance, weather, etc. It offers high-level modeling features for probabilistic programming and Bayesian inference. Lea has several original features: the storage of probabilities as integer weights, an inference algorithm that produces *exact* results and a strong emphasis on ease-of-use. Lea is lightweight, open-source (LGPL) and pure Python, with support of versions 2 and 3). See project page below for installation, tutorials, examples, etc. What's new in Lea 2.3? ---------------------- Compared to latest version (2.2), few things, although important, have been added. * A new method, 'switch', allows you to make efficient Bayesian networks. For variables having many dependences, there is a dramatic speed improvement regarding the 'buildCPT' method available so far. The new method is fully documented in the wiki page dedicated to Bayesian inference, which has been updated in depth: http://bitbucket.org/piedenis/lea/wiki/LeaPyTutorial2. * A new method, 'internal', allows you to see what's inside any Lea instance (should you be curious of that). * Bugs on some secondary methods have been fixed. * Last but not least, for those of you interested in information theory, two new methods have been added to calculate joint entropy and conditional entropy (aka equivocation): http://bitbucket.org/piedenis/lea/wiki/LeaPyTutorial1#markdown-header-mutual -information-joint-and-conditional-entropy What's *in* Lea? ---------------- Lea uses an original probabilistic inference algorithm called the *Statues algorithm*. This relies on the generator construct, a special case of coroutine, embodied in Python with the 'yield' statement. Should you be interested in this topic: - you could have a look at the MicroLea project, which implements no more than the core Statues algorithm (http://bitbucket.org/piedenis/microlea); - be informed that I've written a paper (draft/unpublished) that describes this algorithm in details; if you required it to me, I can provide you this paper; BTW, I would be glad to receive your feedbacks/advices for a potential submission. Lea project page ---------------- http://bitbucket.org/piedenis/lea Documentation ------------- http://bitbucket.org/piedenis/lea/wiki/Home Download Lea (PyPI) ------------------- http://pypi.python.org/pypi/lea/2.3.4 With the hope that Lea can make the World less uncertain, Pierre Denis