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<document url="distribution.html">
  <properties>
    <title>The Commons Math User Guide - Distributions</title>
  </properties>
  <body>
    <section name="8 Probability Distributions">
      <subsection name="8.1 Overview" href="overview">
        <p>
          Standard distributions are now available in the
          <a href="https://commons.apache.org/proper/commons-statistics/userguide/index.html">
          Commons Statistics</a> component.
        </p>
        <p>
          Commons Math provides
          <ul>
            <li>
              an <a href="../apidocs/org/apache/commons/math4/legacy/distribution/EnumeratedDistribution.html">
              EnumeratedDistribution</a> class that represents discrete distributions of a finite,
              enumerated set of values.
            </li>
            <li>
              a <a href="../apidocs/org/apache/commons/math4/legacy/distribution/MultiVariateNormalDistribution.html">
              MultivariateNormalDistribution</a> interface that represents multivariate Gaussian
              distributions.
            </li>
          </ul>
        </p>
        <p>
          Inverse distribution functions can be computed using the
          <code>inverseCumulativeProbability</code> methods.  For continuous <code>f</code>
          and <code>p</code> a probability, <code>f.inverseCumulativeProbability(p)</code> returns
          <code><ul>
            <li>inf{x in R | P(X&le;x) &ge; p} for 0 &lt; p &lt; 1,</li>
            <li>inf{x in R | P(X&le;x) &gt; 0} for p = 0.</li>
          </ul></code> where <code>X</code> is distributed as <code>f</code>.<br/>
          For discrete <code>f</code>, the definition is the same, with <code>Z</code> (the integers)
          in place of <code>R</code>.  Note that in the discrete case, the &ge; in the definition
          can make a difference when <code>p</code> is an attained value of the distribution.
        </p>
      </subsection>

      <subsection name="8.2 Generating data like an input file"
                  href="empirical">
        <p>
          Using the <code>EmpiricalDistribution</code> class, you can generate data based on
          a given set of values:

          <source>
double[] input = load("data.txt"); // Get some data.
int binCount = 500;
EmpiricalDistribution empDist = EmpiricalDistribution.from(binCount, input);
ContinuousDistribution.Sampler sampler = empDist.createSampler(RandomSource.MT.create());
double value = sampler.nextDouble(); </source>

          The probability density function is estimated from the data passed as input.
          The estimation method is essentially the
          <a href="http://nedwww.ipac.caltech.edu/level5/March02/Silverman/Silver2_6.html">
            Variable Kernel Method</a> with Gaussian smoothing.
          The created sampler will return random values whose probability distribution
          matches the empirical distribution (i.e. if you generate a large number of
          such values, their distribution should "look like" the distribution of the
          values in the input file.
          The input values are not stored in memory.
        </p>
      </subsection>
    </section>
  </body>
</document>