gov.sandia.cognition.statistics.distribution

Class BetaDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<NumberType>

gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<java.lang.Double>

gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution

gov.sandia.cognition.statistics.distribution.BetaDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<java.lang.Double>, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, ComputableDistribution<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, EstimableDistribution<java.lang.Double,BetaDistribution>, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

BetaDistribution.CDF, BetaDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Beta distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Beta_distribution")
public class BetaDistribution
extends AbstractClosedFormSmoothUnivariateDistribution
implements EstimableDistribution<java.lang.Double,BetaDistribution>

Computes the Beta-family of probability distributions.

Since:

2.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	BetaDistribution.CDF CDF of the Beta-family distribution
static class	BetaDistribution.MomentMatchingEstimator Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.
static class	BetaDistribution.PDF Beta distribution probability density function
static class	BetaDistribution.WeightedMomentMatchingEstimator Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_ALPHA Default alpha, 2.0.
static double	DEFAULT_BETA Default beta, 2.0.

Constructor Summary

Constructors

Constructor and Description
BetaDistribution() Default constructor.
BetaDistribution(BetaDistribution other) Copy Constructor
BetaDistribution(double alpha, double beta) Creates a new instance of BetaDistribution

Method Summary

Modifier and Type	Method and Description
BetaDistribution	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Sets the parameters of the distribution
Vector	convertToVector() Gets the parameters of the distribution
double	getAlpha() Getter for alpha
double	getBeta() Getter for beta
BetaDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
BetaDistribution.MomentMatchingEstimator	getEstimator() Gets an estimator associated with this distribution.
java.lang.Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
double	getMeanAsDouble() Gets the mean of the distribution as a double.
java.lang.Double	getMinSupport() Gets the minimum support (domain or input) of the distribution.
BetaDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getVariance() Gets the variance of the distribution.
double	sampleAsDouble(java.util.Random random) Samples a value from this distribution as a double.
void	sampleInto(java.util.Random random, double[] output, int start, int length) Samples values from this distribution as an array of doubles.
void	setAlpha(double alpha) Setter for alpha
void	setBeta(double beta) Setter for beta

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

getMean, sampleAsDoubles, sampleInto

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

sample, sample

Methods inherited from class java.lang.Object

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

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

sample, sample, sampleInto

Field Detail

DEFAULT_ALPHA

public static final double DEFAULT_ALPHA

Default alpha, 2.0.

See Also:

Constant Field Values

DEFAULT_BETA

public static final double DEFAULT_BETA

Default beta, 2.0.

See Also:

Constant Field Values

Constructor Detail

BetaDistribution

public BetaDistribution()

Default constructor.

BetaDistribution

public BetaDistribution(double alpha,
                        double beta)

Creates a new instance of BetaDistribution

Parameters:

alpha - Alpha shape parameter, must be greater than 0 (typically greater than 1)

beta - Beta shape parameter, must be greater than 0 (typically greater than 1)

BetaDistribution

public BetaDistribution(BetaDistribution other)

Copy Constructor

Parameters:

other - BetaDistribution to copy

Method Detail

clone

public BetaDistribution 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<java.lang.Double>

Returns:

A clone of this object.

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a double.

Specified by:

getMeanAsDouble in interface UnivariateDistribution<java.lang.Double>

Returns:

The mean as a double.

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<java.lang.Double>

Returns:

Variance of the distribution.

sampleAsDouble

public double sampleAsDouble(java.util.Random random)

Description copied from interface: SmoothUnivariateDistribution

Samples a value from this distribution as a double. This is a convenience method to potentially avoid boxing.

Specified by:

sampleAsDouble in interface SmoothUnivariateDistribution

Overrides:

sampleAsDouble in class AbstractClosedFormSmoothUnivariateDistribution

Parameters:

random - Random number generator to use.

Returns:

A value sampled from this distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       double[] output,
                       int start,
                       int length)

Description copied from interface: SmoothUnivariateDistribution

Samples values from this distribution as an array of doubles. This is a convenience method to potentially avoid boxing.

Specified by:

sampleInto in interface SmoothUnivariateDistribution

Parameters:

random - Random number generator to use.

output - The array to write the result into. Cannot be null.

start - The offset in the array to start writing at. Cannot be negative.

length - The number of values to sample. Cannot be negative.

convertToVector

public Vector convertToVector()

Gets the parameters of the distribution

Specified by:

convertToVector in interface Vectorizable

Returns:

2-dimensional Vector with [alpha beta]

convertFromVector

public void convertFromVector(Vector parameters)

Sets the parameters of the distribution

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - 2-dimensional Vector with [alpha beta]

getAlpha

public double getAlpha()

Getter for alpha

Returns:

Alpha shape parameter, must be greater than 0 (typically greater than 1)

setAlpha

public void setAlpha(double alpha)

Setter for alpha

Parameters:

alpha - Alpha shape parameter, must be greater than 0 (typically greater than 1)

getBeta

public double getBeta()

Getter for beta

Returns:

Beta shape parameter, must be greater than 0 (typically greater than 1)

setBeta

public void setBeta(double beta)

Setter for beta

Parameters:

beta - Beta shape parameter, must be greater than 0 (typically greater than 1)

getCDF

public BetaDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<java.lang.Double>

Specified by:

getCDF in interface SmoothUnivariateDistribution

Specified by:

getCDF in interface UnivariateDistribution<java.lang.Double>

Returns:

CDF of the scalar distribution.

getProbabilityFunction

public BetaDistribution.PDF 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<java.lang.Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Returns:

Distribution function associated with this Distribution.

getMinSupport

public java.lang.Double getMinSupport()

Description copied from interface: UnivariateDistribution

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

Specified by:

getMinSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getMaxSupport

public java.lang.Double getMaxSupport()

Description copied from interface: UnivariateDistribution

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

Specified by:

getMaxSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getEstimator

public BetaDistribution.MomentMatchingEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<java.lang.Double,BetaDistribution>

Returns:

A distribution estimator associated for this distribution.