Java example source code file (distribution.xml)
The distribution.xml Java example source code
<?xml version="1.0"?> <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> <?xml-stylesheet type="text/xsl" href="./xdoc.xsl"?> <document url="distribution.html"> <properties> <title>The Commons Math User Guide - Distributions </properties> <body> <section name="8 Probability Distributions"> <subsection name="8.1 Overview" href="overview"> <p> The distributions package provides a framework and implementations for some commonly used probability distributions. Continuous univariate distributions are represented by implementations of the <a href="../apidocs/org/apache/commons/math3/distribution/RealDistribution.html">RealDistribution interface. Discrete distributions implement <a href="../apidocs/org/apache/commons/math3/distribution/IntegerDistribution.html">IntegerDistribution (values must be mapped to integers) and there is an <a href="../apidocs/org/apache/commons/math3/distribution/EnumeratedDistribution.html">EnumeratedDistribution 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 interface. </p> <p> An overview of available continuous distributions:<br/> <img src="../images/userguide/real_distribution_examples.png" alt="Overview of continuous distributions"/> </p> </subsection> <subsection name="8.2 Distribution Framework" href="distributions"> <p> The distribution framework provides the means to compute probability density functions (<code>density(·)), probability mass functions (<code>probability(·)) and distribution functions (<code>cumulativeProbability(·)) for both discrete (integer-valued) and continuous probability distributions. The framework also allows for the computation of inverse cumulative probabilities and sampling from distributions. </p> <p> For an instance <code>f of a distribution
For discrete <code>f, the definition is the same, with
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