maxent: Compute Maximum Entropy Distributions

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.

Modules

[Index]

Downloads

• maxent-0.4.0.0.tar.gz [browse] (Cabal source package)
• Package description (as included in the package)

Maintainer's Corner

Package maintainers

• JonathanFischoff, pash

For package maintainers and hackage trustees

• edit package information

Candidates

• No Candidates