maxentropy: Maximum entropy and minimum divergence models in Python

Purpose

This package helps you to construct a probability distribution
(Bayesian prior) from prior information that you encode as
generalized moment constraints.

You can use it to either:

find the flattest distribution that meets your constraints, using the
maximum entropy principle (discrete distributions only)
or find the “closest” model to a given prior model (in a KL divergence
sense) that also satisfies your additional constraints.

Background

The maximum entropy principle has been shown [Cox 1982, Jaynes 2003] to be the unique consistent approach to
constructing a discrete probability distribution from prior information that is available as “testable information”.

If the constraints have the form of linear moment constraints, then
the principle gives rise to a unique probability distribution of
exponential form. Most well-known probability distributions are
special cases of maximum entropy distributions. This includes
uniform, geometric, exponential, Pareto, normal, von Mises, Cauchy,
and others: see
here.

Examples: constructing a prior subject to known constraints

See the notebooks folder.

Quickstart guide

This is a good place to start: Loaded die example (scikit-learn estimator API)

History

This package previously lived in SciPy
(http://scipy.org) as scipy.maxentropy from versions v0.5 to v0.10.
It was under-maintained and removed from SciPy v0.11. It has since been
resurrected and refactored to use the scikit-learn Estimator inteface.