Package org.apache.commons.statistics.distribution

Class PoissonDistribution

java.lang.Object
org.apache.commons.statistics.distribution.PoissonDistribution
All Implemented Interfaces:
DiscreteDistribution
public class PoissonDistribution
extends java.lang.Object
Implementation of the Poisson distribution.

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.DiscreteDistribution

    DiscreteDistribution.Sampler

  • Constructor Summary

    Constructors 
    Constructor Description
    PoissonDistribution​(double p)
    Creates a new Poisson distribution with specified mean.

  • Method Summary

    Modifier and Type Method Description
    DiscreteDistribution.Sampler createSampler​(UniformRandomProvider rng)
    Creates a sampler.
    double cumulativeProbability​(int x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
    double getMean()
    Gets the mean of this distribution.
    int getSupportLowerBound()
    Gets the lower bound of the support.
    int getSupportUpperBound()
    Gets the upper bound of the support.
    double getVariance()
    Gets the variance of this distribution.
    int inverseCumulativeProbability​(double p)
    Computes the quantile function of this distribution.
    boolean isSupportConnected()
    Indicates whether the support is connected, i.e.
    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.
    double normalApproximateProbability​(int x)
    Calculates the Poisson distribution function using a normal approximation.
    double probability​(int x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X = x).
    double probability​(int x0, int x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1).
    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.

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • PoissonDistribution

      public PoissonDistribution​(double p)
      Creates a new Poisson distribution with specified mean.
      Parameters:
      p - the Poisson mean
      Throws:
      java.lang.IllegalArgumentException - if p <= 0.

  • Method Details

    • probability

      public double probability​(int 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.
      Parameters:
      x - Point at which the PMF is evaluated.
      Returns:
      the value of the probability mass function at x.

    • 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.
      Parameters:
      x - Point at which the PMF is evaluated.
      Returns:
      the logarithm of the value of the probability mass function at x.

    • cumulativeProbability

      public double cumulativeProbability​(int 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 (cumulative) distribution function (CDF) for this distribution.
      Parameters:
      x - Point at which the CDF is evaluated.
      Returns:
      the probability that a random variable with this distribution takes a value less than or equal to x.

    • normalApproximateProbability

      public double normalApproximateProbability​(int x)
      Calculates the Poisson distribution function using a normal approximation. The N(mean, sqrt(mean)) distribution is used to approximate the Poisson distribution. The computation uses "half-correction" (evaluating the normal distribution function at x + 0.5).
      Parameters:
      x - Upper bound, inclusive.
      Returns:
      the distribution function value calculated using a normal approximation.

    • getMean

      public double getMean()
      Gets the mean of this distribution.
      Returns:
      the mean, or Double.NaN if it is not defined.

    • getVariance

      public double getVariance()
      Gets the variance of this distribution. For mean parameter p, the variance is p.
      Returns:
      the variance, or Double.NaN if it is not defined.

    • getSupportLowerBound

      public int getSupportLowerBound()
      Gets the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0), i.e. inf {x in Z | P(X <= x) > 0}. By convention, Integer.MIN_VALUE should be substituted for negative infinity. The lower bound of the support is always 0 no matter the mean parameter.
      Returns:
      lower bound of the support (always 0)

    • getSupportUpperBound

      public int getSupportUpperBound()
      Gets the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1), i.e. inf {x in R | P(X <= x) = 1}. By convention, Integer.MAX_VALUE should be substituted for positive infinity. The upper bound of the support is positive infinity, regardless of the parameter values. There is no integer infinity, so this method returns Integer.MAX_VALUE.
      Returns:
      upper bound of the support (always Integer.MAX_VALUE for positive infinity)

    • isSupportConnected

      public boolean isSupportConnected()
      Indicates whether the support is connected, i.e. whether all integers between the lower and upper bound of the support are included in the support. The support of this distribution is connected.
      Returns:
      true

    • 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.

    • probability

      public double probability​(int x0, int x1)
      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.

    • inverseCumulativeProbability

      public int inverseCumulativeProbability​(double p)
      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).

    • 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.