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Added some info on sampling. 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1597357 13f79535-47bb-0310-9956-ffa450edef68
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Phil Steitz 2014-05-24 21:21:59 +00:00
parent 17bac30082
commit 3e0f532e18
1 changed files with 20 additions and 2 deletions
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22
src/site/xdoc/userguide/distribution.xml
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@ -28,7 +28,16 @@
<subsection name="8.1 Overview" href="overview"> <subsection name="8.1 Overview" href="overview">
<p> <p>
The distributions package provides a framework and implementations for some commonly used The distributions package provides a framework and implementations for some commonly used
probability distributions. probability distributions. Continuous univariate distributions are represented by implementations of
the <a href="../apidocs/org/apache/commons/math3/distribution/RealDistribution.html">RealDistribution</a>
interface. Discrete distributions implement
<a href="../apidocs/org/apache/commons/math3/distribution/IntegerDistribution.html">IntegerDistribution</a>
(values must be mapped to integers) and there is an
<a href="../apidocs/org/apache/commons/math3/distribution/EnumeratedDistribution.html">EnumeratedDistribution</a>
class representing discrete distributions with a finite, enumerated set of values. Finally, multivariate
real-valued distributions can be represented via the
<a href="../apidocs/org/apache/commons/math3/distribution/MultiVariateRealDistribution.html">MultivariateRealDistribution</a>
interface.
</p> </p>
<p> <p>
An overview of available continuous distributions:<br/> An overview of available continuous distributions:<br/>
@ -42,7 +51,8 @@
(<code>probability(&middot;)</code>) and distribution functions (<code>probability(&middot;)</code>) and distribution functions
(<code>cumulativeProbability(&middot;)</code>) for both (<code>cumulativeProbability(&middot;)</code>) for both
discrete (integer-valued) and continuous probability distributions. discrete (integer-valued) and continuous probability distributions.
The framework also allows for the computation of inverse cumulative probabilities. The framework also allows for the computation of inverse cumulative probabilities
and sampling from distributions.
</p> </p>
<p> <p>
For an instance <code>f</code> of a distribution <code>F</code>, For an instance <code>f</code> of a distribution <code>F</code>,
@ -62,6 +72,14 @@
<source>TDistribution t = new TDistribution(29); <source>TDistribution t = new TDistribution(29);
double lowerTail = t.cumulativeProbability(-2.656); // P(T(29) &lt;= -2.656) double lowerTail = t.cumulativeProbability(-2.656); // P(T(29) &lt;= -2.656)
double upperTail = 1.0 - t.cumulativeProbability(2.75); // P(T(29) &gt;= 2.75)</source> double upperTail = 1.0 - t.cumulativeProbability(2.75); // P(T(29) &gt;= 2.75)</source>
<p>
All distributions implement a <code>sample()</code> method to support random sampling from the
distribution. Implementation classes expose constructors allowing the default
<a href="../apidocs/org/apache/commons/math3/random/RandomGenerator.html">RandomGenerator</a>
used by the sampling algorithm to be overridden. If sampling is not going to be used, providing
a null <code>RandomGenerator</code> constructor argument will avoid the overhead of initializing
the default generator.
</p>
<p> <p>
Inverse distribution functions can be computed using the Inverse distribution functions can be computed using the
<code>inverseCumulativeProbability</code> methods. For continuous <code>f</code> <code>inverseCumulativeProbability</code> methods. For continuous <code>f</code>