Lea 3.0.0.beta.2 is now released!

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http://pypi.org/project/lea/3.0.0.beta.2What is Lea?

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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, machine learning, education, ...

What's new in Lea 3?

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Compared to latest version (2.3.5), many things have changed to extend the

usability and openness of the library. To name a few:

* ability to choose between different probability representations: floats,

fractions and decimals

* symbolic computation: Lea can now calculate probability *formula* using

the SymPy library (

http://www.sympy.org)

* simpler API and compliance with PEP8 naming convention

* revamped tutorials and examples ->

http://bitbucket.org/piedenis/lea/wiki/HomeHere is a short sample. A biased coins is flipped with 1/4 chance to be

'head'. Suppose that this coin is thrown 6 times. What is the probability to

get no more than two 'heads'? Here is how you could make this calculation in

Lea, using successively float, fraction and symbolic representations:

print (P(lea.binom(6,1/4) <= 2))

# -> 0.83056640625

print (P(lea.binom(6,'1/4') <= 2))

# -> 1701/2048

print (P(lea.binom(6,'p') <= 2))

# -> (p - 1)**4*(10*p**2 + 4*p + 1))

print (P(lea.binom(6,'p') <= 2).subs('p',1/4))

# -> 0.830566406250000

To learn more...

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Lea project page ->

http://bitbucket.org/piedenis/leaDocumentation ->

http://bitbucket.org/piedenis/lea/wiki/HomeLea 3 on PyPI ->

http://pypi.org/project/lea/3.0.0.beta.1With the hope that Lea can make the Universe less uncertain,

Pierre Denis

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