Package org.apache.commons.math4.distribution

Class AbstractRealDistribution

java.lang.Object
org.apache.commons.math4.distribution.AbstractRealDistribution
All Implemented Interfaces:
java.io.Serializable, RealDistribution, ContinuousDistribution
Direct Known Subclasses:
EmpiricalDistribution
public abstract class AbstractRealDistribution
extends java.lang.Object
implements RealDistribution, java.io.Serializable
Base class for probability distributions on the reals. Default implementations are provided for some of the methods that do not vary from distribution to distribution.

This base class provides a default factory method for creating a sampler instance that uses the inversion method for generating random samples that follow the distribution.

Since:
3.0
See Also:
Serialized Form

  • Field Details

  • Constructor Details

  • Method Details

    • probability

      public double probability​(double x0, double x1)
      For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
      Specified by:
      probability in interface ContinuousDistribution
      Parameters:
      x0 - Lower bound (excluded).
      x1 - Upper bound (included).
      Returns:
      the probability that a random variable with this distribution takes a value between x0 and x1, excluding the lower and including the upper endpoint.
      Throws:
      NumberIsTooLargeException - if x0 > x1. The default implementation uses the identity P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)
      Since:
      3.1

    • inverseCumulativeProbability

      public double inverseCumulativeProbability​(double p) throws OutOfRangeException
      Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is
      • inf{x in R | P(X<=x) >= p} for 0 < p <= 1,
      • inf{x in R | P(X<=x) > 0} for p = 0.
      The default implementation returns
      Specified by:
      inverseCumulativeProbability in interface ContinuousDistribution
      Parameters:
      p - Cumulative probability.
      Returns:
      the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
      Throws:
      OutOfRangeException

    • getSolverAbsoluteAccuracy

      protected double getSolverAbsoluteAccuracy()
      Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
      Returns:
      the maximum absolute error in inverse cumulative probability estimates

    • probability

      public double probability​(double x)
      For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.
      Specified by:
      probability in interface ContinuousDistribution
      Parameters:
      x - Point at which the PMF is evaluated.
      Returns:
      zero.
      Since:
      3.1

    • logDensity

      public double logDensity​(double x)
      Returns the natural logarithm of the probability density function (PDF) of this distribution evaluated at the specified point x.

      The default implementation simply computes the logarithm of density(x).

      Specified by:
      logDensity in interface ContinuousDistribution
      Parameters:
      x - Point at which the PDF is evaluated.
      Returns:
      the logarithm of the value of the probability density function at x.

    • sample

      public static double[] sample​(int n, ContinuousDistribution.Sampler sampler)
      Utility function for allocating an array and filling it with n samples generated by the given sampler.
      Parameters:
      n - Number of samples.
      sampler - Sampler.
      Returns:
      an array of size n.

    • createSampler

      public ContinuousDistribution.Sampler createSampler​(UniformRandomProvider rng)
      Creates a sampler.
      Specified by:
      createSampler in interface ContinuousDistribution
      Parameters:
      rng - Generator of uniformly distributed numbers.
      Returns:
      a sampler that produces random numbers according this distribution.