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|
|Dependencies||ad (==3.2.*), base (==4.6.*), nonlinear‑optimization (==0.3.*), vector (==0.9.*) [details]|
|Uploaded||by JonathanFischoff at Tue Jan 8 04:13:18 UTC 2013|
|Downloads||3672 total (37 in the last 30 days)|
|Rating||(no votes yet) [estimated by rule of succession]|
|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.
For package maintainers and hackage trustees