Lea documentation

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, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.

All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.

Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.

Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.

Lea 4 requires Python 3.8+. For earlier Python versions (2.6+), Lea 3 can be used.

Please visit Lea home page for tutorials and resources.

Features:

• discrete probability distributions - support: any object!

• probabilistic arithmetic: arithmetic, comparison, logical operators and functions

• probabilistic programming (PP): Bayesian reasoning, CPT, BN, JPD, MC sampling, Markov chains, …

• machine learning: maximum likelihood & EM algorithms

• standard indicators + information theory

• multiple probability representations: float, decimal, fraction, …

• symbolic computation, i.e. producing probability formulas instead of numbers, using the SymPy library

• exact probabilistic inference based on Python generators

• random sampling

• comprehensive tutorials (Wiki)

• Python 3.8+ supported (for Python 2.6+, use Lea 3)

• open-source - LGPL license

lea module

• lea module
  • Alea
  • Blea
  • Clea
  • Dlea
  • EvidenceCtx
  • Flea
  • Flea1
  • Flea2
  • Flea2a
  • Glea
  • Ilea
  • Lea
  • LeaError
  • Olea
  • P()
  • Pf()
  • Plea
  • Rlea
  • Slea
  • Tlea
  • add_evidence()
  • all_decreasing()
  • all_different()
  • all_equal()
  • all_false()
  • all_increasing()
  • all_pairwise_verify()
  • all_strict_decreasing()
  • all_strict_increasing()
  • all_true()
  • all_verify()
  • any_false()
  • any_true()
  • any_verify()
  • bernoulli()
  • binom()
  • clear_evidence()
  • coerce()
  • cpt()
  • declare_namespace()
  • dist_l1()
  • dist_l2()
  • event()
  • evidence
  • func_wrapper()
  • get_active_conditions()
  • get_prob_type()
  • has_evidence()
  • if_()
  • interval()
  • joint()
  • joint_entropy()
  • max_of()
  • min_of()
  • mutual_information()
  • pmf()
  • poisson()
  • pop_evidence()
  • read_bif_file()
  • read_csv_file()
  • read_pandas_df()
  • reduce_all()
  • set_display_options()
  • set_prob_type()
  • vals()
  • lea.leaf submodule
  • lea.markov submodule