maxent 0.2.0.0 → 0.2.0.1
raw patch · 2 files changed
+50/−27 lines, 2 filesPVP ok
version bump matches the API change (PVP)
API changes (from Hackage documentation)
Files
- maxent.cabal +38/−21
- src/MaxEnt.hs +12/−6
maxent.cabal view
@@ -10,32 +10,49 @@ -- PVP summary: +-+------- breaking API changes -- | | +----- non-breaking API additions -- | | | +--- code changes with no API change-version: 0.2.0.0+version: 0.2.0.1 -- A short (one-line) description of the package. synopsis: Compute Maximum Entropy Distributions -- A longer description of the package.-description: Use this package to compute maximum entropy distributions given 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]- - +description: + 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. -- URL for the project homepage or repository. homepage: https://github.com/jfischoff/maxent
src/MaxEnt.hs view
@@ -2,7 +2,9 @@ -- The maximum entropy method, or MAXENT, is variational approach for computing probability -- distributions given a list of moment, or expected value, constraints. -- --- Here are a link for background info.+-- 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> -- @@ -12,14 +14,18 @@ -- -- Here is a the example from Probability the Logic of Science -- --- > maxent ([1,2,3], [average 1.5])--- +-- @+-- 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])--- +--+-- @+-- 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.