Package org.hipparchus.distribution.discrete

Class BinomialDistribution

java.lang.Object

org.hipparchus.distribution.discrete.AbstractIntegerDistribution

org.hipparchus.distribution.discrete.BinomialDistribution

All Implemented Interfaces:

Serializable, IntegerDistribution

public class BinomialDistribution
extends AbstractIntegerDistribution

Implementation of the binomial distribution.

See Also:

Binomial distribution (Wikipedia), Binomial Distribution (MathWorld), Serialized Form

Constructor Summary

Constructors

Constructor	Description
BinomialDistribution(int trials, double p)	Create a binomial distribution with the given number of trials and probability of success.

Method Summary

Modifier and Type	Method	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	getNumberOfTrials()	Access the number of trials 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

BinomialDistribution

public BinomialDistribution(int trials,
                            double p)
                     throws MathIllegalArgumentException

Create a binomial distribution with the given number of trials and probability of success.

Parameters:

trials - Number of trials.

p - Probability of success.

Throws:

MathIllegalArgumentException - if trials < 0.

MathIllegalArgumentException - if p < 0 or p > 1.

Method Detail

getNumberOfTrials

public int getNumberOfTrials()

Access the number of trials for this distribution.

Returns:

the number of trials.

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 n trials and probability parameter p, the mean is n * 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 n trials and probability parameter p, the variance is n * p * (1 - p).

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 except for the probability parameter p = 1.

Returns:

lower bound of the support (0 or the number of trials)

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 the number of trials except for the probability parameter p = 0.

Returns:

upper bound of the support (number of trials or 0)

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