gov.sandia.cognition.statistics.distribution
Class NegativeBinomialDistribution

java.lang.Object
  gov.sandia.cognition.util.AbstractCloneableSerializable
      gov.sandia.cognition.statistics.AbstractDistribution<NumberType>
          gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<Number>
              gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<Number>, ClosedFormDiscreteUnivariateDistribution<Number>, ClosedFormDistribution<Number>, ClosedFormUnivariateDistribution<Number>, ComputableDistribution<Number>, DiscreteDistribution<Number>, Distribution<Number>, DistributionWithMean<Number>, EstimableDistribution<Number,NegativeBinomialDistribution>, UnivariateDistribution<Number>, CloneableSerializable, Serializable, Cloneable

Direct Known Subclasses:

NegativeBinomialDistribution.CDF, NegativeBinomialDistribution.PMF

@PublicationReference(author="Wikipedia",
                      title="Negative binomial distribution",
                      type=WebPage,
                      year=2010,
                      url="http://en.wikipedia.org/wiki/Negative_binomial_distribution")
public class NegativeBinomialDistribution
extends AbstractClosedFormUnivariateDistribution<Number>
implements ClosedFormDiscreteUnivariateDistribution<Number>, EstimableDistribution<Number,NegativeBinomialDistribution>

Negative binomial distribution, also known as the Polya distribution, gives the number of successes of a series of Bernoulli trials before recording a given number of failures.

Since:

3.0

See Also:

Serialized Form

Nested Class Summary

static class	NegativeBinomialDistribution.CDF CDF of the NegativeBinomialDistribution
static class	NegativeBinomialDistribution.MaximumLikelihoodEstimator Maximum likelihood estimator of the distribution
static class	NegativeBinomialDistribution.PMF PMF of the NegativeBinomialDistribution.
static class	NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator Weighted maximum likelihood estimator of the distribution

Field Summary

static double	DEFAULT_P Default p, 0.5.
static double	DEFAULT_R Default r, 1.0.
protected double	p Probability of a positive outcome (Bernoulli probability), [0,1]
protected double	r Number of trials before the experiment is stopped, must be greater than zero.

Constructor Summary

NegativeBinomialDistribution()
Creates a new instance of NegativeBinomialDistribution

NegativeBinomialDistribution(double r, double p)
Creates a new instance of NegativeBinomialDistribution

NegativeBinomialDistribution(NegativeBinomialDistribution other)
Copy constructor

Method Summary

NegativeBinomialDistribution	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
NegativeBinomialDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
IntegerSpan	getDomain() Returns an object that allows an iteration through the domain (x-axis, independent variable) of the Distribution
int	getDomainSize() Gets the size of the domain.
NegativeBinomialDistribution.MaximumLikelihoodEstimator	getEstimator() Gets an estimator associated with this distribution.
Integer	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
Double	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
Integer	getMinSupport() Gets the minimum support (domain or input) of the distribution.
double	getP() Getter for p
NegativeBinomialDistribution.PMF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getR() Getter for r.
double	getVariance() Gets the variance of the distribution.
ArrayList<? extends Number>	sample(Random random, int numSamples) Draws multiple random samples from the distribution.
void	setP(double p) Setter for p
void	setR(double r) Setter for r.
String	toString()

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample

Field Detail

DEFAULT_P

public static final double DEFAULT_P

Default p, 0.5.

See Also:

Constant Field Values

DEFAULT_R

public static final double DEFAULT_R

Default r, 1.0.

See Also:

Constant Field Values

r

protected double r

Number of trials before the experiment is stopped, must be greater than zero.

p

protected double p

Probability of a positive outcome (Bernoulli probability), [0,1]

Constructor Detail

NegativeBinomialDistribution

public NegativeBinomialDistribution()

Creates a new instance of NegativeBinomialDistribution

NegativeBinomialDistribution

public NegativeBinomialDistribution(double r,
                                    double p)

Creates a new instance of NegativeBinomialDistribution

Parameters:

r - Number of trials before the experiment is stopped, must be greater than zero.

p - Probability of a positive outcome (Bernoulli probability), [0,1]

NegativeBinomialDistribution

public NegativeBinomialDistribution(NegativeBinomialDistribution other)

Copy constructor

Parameters:

other - NegativeBinomialDistribution to copy

Method Detail

clone

public NegativeBinomialDistribution clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractClosedFormUnivariateDistribution<Number>

Returns:

A clone of this object.

getP

public double getP()

Getter for p

Returns:

Probability of a positive outcome (Bernoulli probability), [0,1]

setP

public void setP(double p)

Setter for p

Parameters:

p - Probability of a positive outcome (Bernoulli probability), [0,1]

getR

public double getR()

Getter for r.

Returns:

Number of trials before the experiment is stopped, must be greater than zero.

setR

public void setR(double r)

Setter for r.

Parameters:

r - Number of trials before the experiment is stopped, must be greater than zero.

getMean

public Double getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<Number>

Returns:

Mean of the distribution.

sample

public ArrayList<? extends Number> sample(Random random,
                                          int numSamples)

Description copied from interface: Distribution

Draws multiple random samples from the distribution. It is generally more efficient to use this multiple-sample method than multiple calls of the single-sample method. (But not always.)

Specified by:

sample in interface Distribution<Number>

Parameters:

random - Random-number generator to use in order to generate random numbers.

numSamples - Number of samples to draw from the distribution.

Returns:

Samples drawn according to this distribution.

getCDF

public NegativeBinomialDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<Number>

Specified by:

getCDF in interface UnivariateDistribution<Number>

Returns:

CDF of the scalar distribution.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

getMinSupport

public Integer getMinSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMinSupport in interface UnivariateDistribution<Number>

Returns:

Minimum support.

getMaxSupport

public Integer getMaxSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMaxSupport in interface UnivariateDistribution<Number>

Returns:

Minimum support.

getVariance

public double getVariance()

Description copied from interface: UnivariateDistribution

Gets the variance of the distribution. This is sometimes called the second central moment by more pedantic people, which is equivalent to the square of the standard deviation.

Specified by:

getVariance in interface UnivariateDistribution<Number>

Returns:

Variance of the distribution.

getDomain

public IntegerSpan getDomain()

Description copied from interface: DiscreteDistribution

Returns an object that allows an iteration through the domain (x-axis, independent variable) of the Distribution

Specified by:

getDomain in interface DiscreteDistribution<Number>

Returns:

Collection that enumerates each value that the domain can take

getDomainSize

public int getDomainSize()

Description copied from interface: DiscreteDistribution

Gets the size of the domain.

Specified by:

getDomainSize in interface DiscreteDistribution<Number>

Returns:

The size of the domain.

getProbabilityFunction

public NegativeBinomialDistribution.PMF getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<Number>

Specified by:

getProbabilityFunction in interface DiscreteDistribution<Number>

Returns:

Distribution function associated with this Distribution.

getEstimator

public NegativeBinomialDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<Number,NegativeBinomialDistribution>

Returns:

A distribution estimator associated for this distribution.

toString

public String toString()

Overrides:

toString in class Object