java.lang.Object
- org.apache.commons.statistics.distribution.HypergeometricDistribution

All Implemented Interfaces:

DiscreteDistribution
```
public final class HypergeometricDistribution
extends Object
```
Implementation of the hypergeometric distribution.
The probability mass function of $X$ is:
$f (k; N, K, n) = \frac{(\binom{K}{k}) (\binom{N - K}{n - k})}{(\binom{N}{n})}$
for $N \in {0, 1, 2, \dots}$ the population size, $K \in {0, 1, \dots, N}$ the number of success states, $n \in {0, 1, \dots, N}$ the number of samples, $k \in {max (0, n + K - N), \dots, min (n, K)}$ the number of successes, and
$(\binom{a}{b}) = \frac{a!}{b! (a - b)!}$
is the binomial coefficient.

See Also:

Hypergeometric distribution (Wikipedia), Hypergeometric distribution (MathWorld)

Nested Class Summary
- Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.DiscreteDistribution
  DiscreteDistribution.Sampler

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`DiscreteDistribution.Sampler`	`createSampler(org.apache.commons.rng.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`	`getNumberOfSuccesses()`	Gets the number of successes parameter of this distribution.
`int`	`getPopulationSize()`	Gets the population size parameter of this distribution.
`int`	`getSampleSize()`	Gets the sample size parameter 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.
`int`	`inverseSurvivalProbability(double p)`	Computes the inverse survival probability function of this distribution.
`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.
`static HypergeometricDistribution`	`of(int populationSize, int numberOfSuccesses, int sampleSize)`	Creates a hypergeometric distribution.
`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)`.
`double`	`survivalProbability(int x)`	For a random variable `X` whose values are distributed according to this distribution, this method returns `P(X > x)`.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Method Detail
  - of
```
public static HypergeometricDistribution of(int populationSize,
                                            int numberOfSuccesses,
                                            int sampleSize)
```
    Creates a hypergeometric distribution.
    
    Parameters:
    
    populationSize - Population size.
    
    numberOfSuccesses - Number of successes in the population.
    
    sampleSize - Sample size.
    
    Returns:
    
    the distribution
    
    Throws:
    
    IllegalArgumentException - if numberOfSuccesses < 0, or populationSize <= 0 or numberOfSuccesses > populationSize, or sampleSize > populationSize.
  - getPopulationSize
```
public int getPopulationSize()
```
    Gets the population size parameter of this distribution.
    
    Returns:
    
    the population size.
  - getNumberOfSuccesses
```
public int getNumberOfSuccesses()
```
    Gets the number of successes parameter of this distribution.
    
    Returns:
    
    the number of successes.
  - getSampleSize
```
public int getSampleSize()
```
    Gets the sample size parameter of this distribution.
    
    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 - Point at which the PMF is evaluated.
    
    Returns:
    
    the value of the probability mass function at x.
  - 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)
    Special cases:
    
    returns 0.0 if x0 == x1;
    returns probability(x1) if x0 + 1 == x1;
    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 takes a value between x0 and x1, excluding the lower and including the upper endpoint.
  - 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.
  - survivalProbability
```
public double survivalProbability(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 complementary cumulative distribution function.
    By default, this is defined as 1 - cumulativeProbability(x), but the specific implementation may be more accurate.
    
    Parameters:
    
    x - Point at which the survival function is evaluated.
    
    Returns:
    
    the probability that a random variable with this distribution takes a value greater than x.
  - 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:
    $x = {\begin{cases} inf {x \in Z : P (X \leq x) \geq p} & for 0 < p \leq 1 \\ inf {x \in Z : P (X \leq x) > 0} & for p = 0 \end{cases}$
    If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. In this case the result of cumulativeProbability(x) called using the returned p-quantile may not compute the original p.
    The default implementation returns:
    
    DiscreteDistribution.getSupportLowerBound() for p = 0,
    
    DiscreteDistribution.getSupportUpperBound() for p = 1, or
    
    the result of a binary search between the lower and upper bound using cumulativeProbability(x). The bounds may be bracketed for efficiency.
    Specified by:
    
    inverseCumulativeProbability in interface DiscreteDistribution
    
    Parameters:
    
    p - Cumulative probability.
    
    Returns:
    
    the smallest p-quantile of this distribution (largest 0-quantile for p = 0).
  - inverseSurvivalProbability
```
public int inverseSurvivalProbability(double p)
```
    Computes the inverse survival probability function of this distribution. For a random variable X distributed according to this distribution, the returned value is:
    $x = {\begin{cases} inf {x \in Z : P (X > x) \leq p} & for 0 \leq p < 1 \\ inf {x \in Z : P (X > x) < 1} & for p = 1 \end{cases}$
    If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. In this case the result of survivalProbability(x) called using the returned (1-p)-quantile may not compute the original p.
    By default, this is defined as inverseCumulativeProbability(1 - p), but the specific implementation may be more accurate.
    The default implementation returns:
    
    DiscreteDistribution.getSupportLowerBound() for p = 1,
    
    DiscreteDistribution.getSupportUpperBound() for p = 0, or
    
    the result of a binary search between the lower and upper bound using survivalProbability(x). The bounds may be bracketed for efficiency.
    Specified by:
    
    inverseSurvivalProbability in interface DiscreteDistribution
    
    Parameters:
    
    p - Cumulative probability.
    
    Returns:
    
    the smallest (1-p)-quantile of this distribution (largest 0-quantile for p = 1).
  - getMean
```
public double getMean()
```
    Gets the mean of this distribution.
    For population size $N$ , number of successes $K$ , and sample size $n$ , the mean is:
    $n \frac{K}{N}$
    
    Returns:
    
    the mean.
  - getVariance
```
public double getVariance()
```
    Gets the variance of this distribution.
    For population size $N$ , number of successes $K$ , and sample size $n$ , the variance is:
    $n \frac{K}{N} \frac{N - K}{N} \frac{N - n}{N - 1}$
    
    Returns:
    
    the variance.
  - getSupportLowerBound
```
public int getSupportLowerBound()
```
    Gets the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0), i.e. $inf {x \in Z : P (X \leq x) > 0}$ . By convention, Integer.MIN_VALUE should be substituted for negative infinity.
    For population size $N$ , number of successes $K$ , and sample size $n$ , the lower bound of the support is $max {0, n + K - N}$ .
    
    Returns:
    
    lower bound of the support
  - getSupportUpperBound
```
public int getSupportUpperBound()
```
    Gets the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1), i.e. $inf {x \in Z : P (X \leq x) = 1}$ . By convention, Integer.MAX_VALUE should be substituted for positive infinity.
    For number of successes $K$ , and sample size $n$ , the upper bound of the support is $min {n, K}$ .
    
    Returns:
    
    upper bound of the support
  - createSampler
```
public DiscreteDistribution.Sampler createSampler(org.apache.commons.rng.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.

Class HypergeometricDistribution

Nested Class Summary

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

Method Summary

Methods inherited from class java.lang.Object

Method Detail

of

getPopulationSize

getNumberOfSuccesses

getSampleSize

probability

probability

logProbability

cumulativeProbability

survivalProbability

inverseCumulativeProbability

inverseSurvivalProbability

getMean

getVariance

getSupportLowerBound

getSupportUpperBound

createSampler