Package org.apache.commons.statistics.distribution

Class PoissonDistribution

    • 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 Detail

      • 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 Detail

      • 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

      • 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.

      • 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.