# maxent: Compute Maximum Entropy Distributions

Versions | 0.1.0.0, 0.1.0.1, 0.2.0.0, 0.2.0.1, 0.3.0.1, 0.3.1.1, 0.4.0.0, 0.6.0.0, 0.6.0.1, 0.6.0.3, 0.6.0.4, 0.7 |
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Dependencies | ad (==3.2.*), base (==4.6.*), nonlinear‑optimization (==0.3.*), vector (==0.9.*) [details] |

License | BSD-3-Clause |

Author | Jonathan Fischoff |

Maintainer | jonathangfischoff@gmail.com |

Category | Math |

Home page | https://github.com/jfischoff/maxent |

Uploaded | by JonathanFischoff at Tue Jan 8 04:13:18 UTC 2013 |

Distributions | NixOS:0.7 |

Downloads | 3672 total (37 in the last 30 days) |

Rating | (no votes yet) [estimated by rule of succession] |

Your Rating | |

Status | Docs uploaded by user Build status unknown [no reports yet] Hackage Matrix CI |

The maximum entropy method, or MAXENT, is variational approach for computing probability distributions given a list of moment, or expected value, constraints.

Here are some links for background info.

A good overview of applications: http://cmm.cit.nih.gov/maxent/letsgo.html

On the idea of maximum entropy in general: http://en.wikipedia.org/wiki/Principle_of_maximum_entropy

Use this package to compute discrete maximum entropy distributions over a list of values and list of constraints.

Here is a the example from Probability the Logic of Science

maxent ([1,2,3], [average 1.5])

Right [0.61, 0.26, 0.11]

The classic dice example

maxent ([1,2,3,4,5,6], [average 4.5])

Right [.05, .07, 0.11, 0.16, 0.23, 0.34]

One can use different constraints besides the average value there.

As for why you want to maximize the entropy to find the probability constraint, I will say this for now. In the case of the average constraint it is a kin to choosing a integer partition with the most interger compositions. I doubt that makes any sense, but I will try to explain more with a blog post soon.

## Downloads

- maxent-0.3.0.1.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)