org.hipparchus.distribution.discrete

Class PascalDistribution

java.lang.Object

org.hipparchus.distribution.discrete.AbstractIntegerDistribution

org.hipparchus.distribution.discrete.PascalDistribution

All Implemented Interfaces:

Serializable, IntegerDistribution

public class PascalDistribution
extends 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

Constructor Summary

Constructors

Constructor and Description
PascalDistribution(int r, double p) Create a Pascal distribution with the given number of successes and probability of success.

Method Summary

Modifier and Type	Method and Description
double	cumulativeProbability(int x) For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
int	getNumberOfSuccesses() Access the number of successes for this distribution.
double	getNumericalMean() Use this method to get the numerical value of the mean of this distribution.
double	getNumericalVariance() Use this method to get the numerical value of the variance of this distribution.
double	getProbabilityOfSuccess() Access the probability of success for this distribution.
int	getSupportLowerBound() Access the lower bound of the support.
int	getSupportUpperBound() Access the upper bound of the support.
boolean	isSupportConnected() Use this method to get information about 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	probability(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 org.hipparchus.distribution.discrete.AbstractIntegerDistribution

inverseCumulativeProbability, probability, solveInverseCumulativeProbability

Methods inherited from class java.lang.Object

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

Constructor Detail

PascalDistribution

public PascalDistribution(int r,
                          double p)
                   throws MathIllegalArgumentException

Create a Pascal distribution with the given number of successes and probability of success.

Parameters:

r - Number of successes.

p - Probability of success.

Throws:

MathIllegalArgumentException - if the number of successes is not positive

MathIllegalArgumentException - if the probability of success is not in the range [0, 1].

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).

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

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