diff --git a/xdocs/userguide/distribution.xml b/xdocs/userguide/distribution.xml index 43e4798d3..b188c3370 100644 --- a/xdocs/userguide/distribution.xml +++ b/xdocs/userguide/distribution.xml @@ -17,7 +17,7 @@ --> - + The Commons Math User Guide - Statistics @@ -60,6 +60,7 @@ BinomialDistribution binomial = factory.createBinomialDistribution(10, .75);GammacreateGammaDistribution
Alpha
Beta
HypergeometriccreateHypogeometricDistribution
Population size
Number of successes in population
Sample size
Normal (Gaussian)createNormalDistribution
Mean
Standard Deviation
+ PoissoncreatePoissonDistribution
Mean
tcreateTDistribution
Degrees of freedom

@@ -89,6 +90,23 @@ double upperTail = 1.0 - t.cumulativeProbability(2.75); // P(T >= 2.75) + +

+ Since there are numerous distributions and Commons-Math only directly supports a handfull, + it may be necessary to extend the distribution framework to satisfy individual needs. It + is recommended that the Distribution, ContinuousDistribution, + DiscreteDistribution, and IntegerDistribution interfaces serve as + base types for any extension. These serve as the basis for all the distributions directly + supported by Commons-Math and using those interfaces for implementation purposes will + insure any extension is compatible with the remainder of Commons-Math. To aid in + implementing a distribution extension, the AbstractDistribution, + AbstractContinuousDistribution, and AbstractIntegerDistribution + provide implementation building blocks and offer a lot of default distribution + functionality. By extending these abstract classes directly, a good portion of the + repetative distribution implementation is already developed and should same time and effort + in developing user defined distributions. +

+
diff --git a/xdocs/userguide/index.xml b/xdocs/userguide/index.xml index 7c97527f7..be2f42ba3 100644 --- a/xdocs/userguide/index.xml +++ b/xdocs/userguide/index.xml @@ -17,7 +17,7 @@ --> - + The Commons Math User Guide - Table of Contents @@ -43,7 +43,6 @@
  • 1.3 Frequency distributions
  • 1.4 Simple regression
  • 1.5 Statistical tests
  • -
  • 1.6 Distribution framework
  • 2. Data Generation