diff --git a/maxent.cabal b/maxent.cabal
--- a/maxent.cabal
+++ b/maxent.cabal
@@ -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
diff --git a/src/MaxEnt.hs b/src/MaxEnt.hs
--- a/src/MaxEnt.hs
+++ b/src/MaxEnt.hs
@@ -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.