ANN: Lea 2.2.0-beta.4

[Pierre Denis]

Hi all!

For those of you interested in probabilities and probabilistic programming, I’m
happy to announce that Lea 2.2.0 is now under beta-test.

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, weather, etc. It
offers several high-level modelling features for probabilistic programming,
including bayesian inference and Markov chains. Lea is open-source (LGPL) and
runs on Python 2 or 3. See project page below for more information
(installation, tutorials, examples, etc).

What’s new?
-----------
Compared to latest version (2.1.2), many things have been made in 2.2.0 to
improve ease-of-use and overall performance, without breaking backward
compatibility. Maybe one of the most notable feature is that you can now get
individual probability very easily, as a fraction or float, thanks to the new
'P' and 'Pf' functions, e.g.

>>> P(dice <= 5)
5/18
>>> Pf(dice <= 5)
0.2777777777777778
>>> P(rain.given(grassWet))
891/2491
>>> Pf(rain.given(grassWet))
0.3576876756322762

New methods allow you to read a CSV file or Pandas dataframe, then build the
corresponding joint probability distribution. Also, Monte-Carlo sampling
estimation is now available, should Lea’s default exact evaluation is
intractable. Most of the new features are documented in a new tutorial on Lea's
wiki (https://bitbucket.org/piedenis/lea/wiki/LeaPyTutorial3).

The latest version, Lea 2.2.0-beta.4, is fairly stable (no known bug) so you can
start to use it and report any problem or dislike, if any.

Lea project page
----------------
https://bitbucket.org/piedenis/lea

Download Lea (PyPI)
-------------------
http://pypi.python.org/pypi/lea

With the hope that Lea can make the Force less uncertain,

Pierre Denis