Package org.hipparchus.distribution.discrete

Class HypergeometricDistribution

    • Constructor Detail

      • HypergeometricDistribution

        public HypergeometricDistribution​(int populationSize,
                                  int numberOfSuccesses,
                                  int sampleSize)
                           throws MathIllegalArgumentException
        Construct a new hypergeometric distribution with the specified population size, number of successes in the population, and sample size.
        Parameters:
        populationSize - Population size.
        numberOfSuccesses - Number of successes in the population.
        sampleSize - Sample size.
        Throws:
        MathIllegalArgumentException - if numberOfSuccesses < 0.
        MathIllegalArgumentException - if populationSize <= 0.
        MathIllegalArgumentException - if numberOfSuccesses > populationSize, or sampleSize > populationSize.

    • Method Detail

      • 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

      • getNumberOfSuccesses

        public int getNumberOfSuccesses()
        Access the number of successes.
        Returns:
        the number of successes.

      • getPopulationSize

        public int getPopulationSize()
        Access the population size.
        Returns:
        the population size.

      • getSampleSize

        public int getSampleSize()
        Access the sample size.
        Returns:
        the sample size.

      • 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).

        Specified by:
        logProbability in interface IntegerDistribution
        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

      • upperCumulativeProbability

        public double upperCumulativeProbability​(int x)
        For this distribution, X, this method returns P(X >= x).
        Parameters:
        x - Value at which the CDF is evaluated.
        Returns:
        the upper tail CDF for this distribution.

      • getNumericalMean

        public double getNumericalMean()
        Use this method to get the numerical value of the mean of this distribution. For population size N, number of successes m, and sample size n, the mean is n * m / N.
        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 population size N, number of successes m, and sample size n, the variance is [n * m * (N - n) * (N - m)] / [N^2 * (N - 1)].
        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}.

        For population size N, number of successes m, and sample size n, the lower bound of the support is max(0, n + m - N).
        Returns:
        lower bound of the support

      • 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}.

        For number of successes m and sample size n, the upper bound of the support is min(m, n).
        Returns:
        upper bound of the support

      • 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