[Python-announce] ANN: Lea 4.0.0 released

[pie....@skynet.be]

Lea 4.0.0 is now released!
--> https://pypi.org/project/lea/

Save the date: Live demo of Lea at FOSDEM'24, Brussels, Feb 4, 2024 (this is
free!)
--> https://fosdem.org/2024/schedule/track/python-devroom/

What is Lea?
-----------------
Lea is a Python module for playing with discrete probability distributions
in an intuitive way. This ranges from simple dice, coin flipping, random
sampling . to Bayesian networks, Probabilistic Programming (PP), machine
learning and symbolic computation. One salient feature of Lea is the ability
to change the probability representation, viz. float, fractions, decimals,
and symbols. Lea can be used for education, AI, PP, etc. Comprehensive
tutorials are available online (see Wiki pages). For a 5 minutes tour, check
out the poster presented at PROBPROG2020 conference:
https://probprog.cc/2020/assets/posters/fri/69.pdf .

What's new in Lea 4.0.0?
---------------------------------
Here are the major changes since Lea 3.4.6:
- optimization of conditional probabilities ('given' method), BN and
fractions' display,
- new "such_that" and "as_depending_on" methods to ease CPT creation (e.g.
the Monty Hall problem)
- persistent display options, including one line display of probability
distributions,
- fixes for mixing different probability representations,
- indicators as method to call with parenthesis (e.g. x.mean() instead of
x.mean),
- extension of P, Pf, given_prob, is_any_of and is_none_of methods,
- improved method documentation and code structure,
- support to Python 3.8+ only (for Python 2.7+, you may still use Lea 3 -->
https://pypi.org/project/lea/3.4.6/ )

For more information, you may check issues starting at #70 on
https://bitbucket.org/piedenis/lea/issues .

Acknowledgment
------------------------
Many, many thanks to Paul Moore, who has given the impetus for this new
version! For several years, Paul has provided invaluable ideas, advice and
contributions to the Lea project.

To learn more...
----------------------
Lea on PyPI --> http://pypi.org/project/lea
Lea project page --> http://bitbucket.org/piedenis/lea
Documentation --> http://bitbucket.org/piedenis/lea/wiki/Home

With the hope that Lea can make this universe less accidental,

Pierre Denis