Package org.apache.commons.math4.distribution

Class AbstractIntegerDistribution

java.lang.Object
org.apache.commons.math4.distribution.AbstractIntegerDistribution
All Implemented Interfaces:
java.io.Serializable, IntegerDistribution, DiscreteDistribution
Direct Known Subclasses:
EnumeratedIntegerDistribution
public abstract class AbstractIntegerDistribution
extends java.lang.Object
implements IntegerDistribution, java.io.Serializable
Base class for integer-valued discrete distributions. Default implementations are provided for some of the methods that do not vary from distribution to distribution.
See Also:
Serialized Form

  • Constructor Details

  • Method Details

    • probability

      public double probability​(int x0, int x1) throws NumberIsTooLargeException
      For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity

      P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)

      Specified by:
      probability in interface DiscreteDistribution
      Parameters:
      x0 - Lower bound (exclusive).
      x1 - Upper bound (inclusive).
      Returns:
      the probability that a random variable with this distribution will take a value between x0 and x1, excluding the lower and including the upper endpoint.
      Throws:
      NumberIsTooLargeException
      Since:
      4.0, was previously named cumulativeProbability

    • inverseCumulativeProbability

      public int 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 Z | P(X<=x) >= p} for 0 < p <= 1,
      • inf{x in Z | P(X<=x) > 0} for p = 0.
      If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. The default implementation returns
      Specified by:
      inverseCumulativeProbability in interface DiscreteDistribution
      Parameters:
      p - Cumulative probability.
      Returns:
      the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
      Throws:
      OutOfRangeException

    • solveInverseCumulativeProbability

      protected int solveInverseCumulativeProbability​(double p, int lower, int upper)
      This is a utility function used by inverseCumulativeProbability(double). It assumes 0 < p < 1 and that the inverse cumulative probability lies in the bracket (lower, upper]. The implementation does simple bisection to find the smallest p-quantile inf{x in Z | P(X<=x) >= p}.
      Parameters:
      p - the cumulative probability
      lower - a value satisfying cumulativeProbability(lower) < p
      upper - a value satisfying p <= cumulativeProbability(upper)
      Returns:
      the smallest p-quantile of this distribution

    • logProbability

      public double logProbability​(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm.

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

      Specified by:
      logProbability in interface DiscreteDistribution
      Parameters:
      x - Point at which the PMF is evaluated.
      Returns:
      the logarithm of the value of the probability mass function at x.

    • sample

      public static int[] sample​(int n, DiscreteDistribution.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 DiscreteDistribution.Sampler createSampler​(UniformRandomProvider rng)
      Creates a sampler.
      Specified by:
      createSampler in interface DiscreteDistribution
      Parameters:
      rng - Generator of uniformly distributed numbers.
      Returns:
      a sampler that produces random numbers according this distribution.