PascalDistribution
org.apache.commons.math3.distribution

Class PascalDistribution

  • All Implemented Interfaces:
    Serializable, IntegerDistribution


    public class PascalDistributionextends AbstractIntegerDistribution

    Implementation of the Pascal distribution. The Pascal distribution is a special case of the Negative Binomial distribution where the number of successes parameter is an integer.

    There are various ways to express the probability mass and distribution functions for the Pascal distribution. The present implementation represents the distribution of the number of failures before r successes occur. This is the convention adopted in e.g. MathWorld, but not in Wikipedia.

    For a random variable X whose values are distributed according to this distribution, the probability mass function is given by
    P(X = k) = C(k + r - 1, r - 1) * p^r * (1 - p)^k,
    where r is the number of successes, p is the probability of success, and X is the total number of failures. C(n, k) is the binomial coefficient (n choose k). The mean and variance of X are
    E(X) = (1 - p) * r / p, var(X) = (1 - p) * r / p^2.
    Finally, the cumulative distribution function is given by
    P(X <= k) = I(p, r, k + 1), where I is the regularized incomplete Beta function.

    See Also:
    Negative binomial distribution (Wikipedia), Negative binomial distribution (MathWorld), Serialized Form
    • Method Detail

      • getNumberOfSuccesses

        public int getNumberOfSuccesses()
        Access the number of successes for this distribution.
        Returns:
        the number of successes.
      • getProbabilityOfSuccess

        public double getProbabilityOfSuccess()
        Access the probability of success for this distribution.
        Returns:
        the probability of success.
      • 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 - the 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. In other words, this method represents the logarithm of the probability mass function (PMF) for the distribution. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of IntegerDistribution.probability(int).

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

        Overrides:
        logProbability in class AbstractIntegerDistribution
        Parameters:
        x - the 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 - the 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
      • getNumericalMean

        public double getNumericalMean()
        Use this method to get the numerical value of the mean of this distribution. For number of successes r and probability of success p, the mean is r * (1 - p) / p.
        Returns:
        the mean or Double.NaN if it is not defined
      • getNumericalVariance

        public double getNumericalVariance()
        Use this method to get the numerical value of the variance of this distribution. For number of successes r and probability of success p, the variance is r * (1 - p) / p^2.
        Returns:
        the variance (possibly Double.POSITIVE_INFINITY or Double.NaN if it is not defined)
      • getSupportLowerBound

        public int getSupportLowerBound()
        Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

        inf {x in Z | P(X <= x) > 0}.

        The lower bound of the support is always 0 no matter the parameters.
        Returns:
        lower bound of the support (always 0)
      • getSupportUpperBound

        public int getSupportUpperBound()
        Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

        inf {x in R | P(X <= x) = 1}.

        The upper bound of the support is always positive infinity no matter the parameters. Positive infinity is symbolized by Integer.MAX_VALUE.
        Returns:
        upper bound of the support (always Integer.MAX_VALUE for positive infinity)
      • isSupportConnected

        public boolean isSupportConnected()
        Use this method to get information about 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

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