gov.sandia.cognition.statistics.distribution

Class MultivariatePolyaDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

gov.sandia.cognition.statistics.distribution.MultivariatePolyaDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDiscreteDistribution<Vector>, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, DiscreteDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

MultivariatePolyaDistribution.PMF

@PublicationReference(author="Wikipedia",
                      title="Multivariate Polya Distribution",
                      type=WebPage,
                      year=2010,
                      url="http://en.wikipedia.org/wiki/Multivariate_Polya_distribution")
public class MultivariatePolyaDistribution
extends AbstractDistribution<Vector>
implements ClosedFormComputableDiscreteDistribution<Vector>

A multivariate Polya Distribution, also known as a Dirichlet-Multinomial model, is a compound distribution where the parameters of a multinomial are drawn from a Dirichlet distribution with fixed parameters and a constant number of trials and then the observations are generated by this multinomial. This is the multivariate generalization of the Beta-Binomial Distribution and is the predictive posterior distribution for a Multinomial Bayesian estimator using its conjugate prior Dirichlet distribution.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MultivariatePolyaDistribution.PMF PMF of the MultivariatePolyaDistribution

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_DIMENSIONALITY Default dimensionality, 2.
static int	DEFAULT_NUM_TRIALS Default number of trials, 1.
protected Vector	parameters Parameters of the Dirichlet distribution, must be at least 2-dimensional and each element must be positive.

Constructor Summary

Constructors

Constructor and Description
MultivariatePolyaDistribution() Creates a new instance of DirichletDistribution
MultivariatePolyaDistribution(int dimensionality, int numTrials) Creates a new instance of MultivariatePolyaDistribution
MultivariatePolyaDistribution(MultivariatePolyaDistribution other) Copy Constructor.
MultivariatePolyaDistribution(Vector parameters, int numTrials) Creates a new instance of MultivariatePolyaDistribution

Method Summary

Modifier and Type	Method and Description
MultivariatePolyaDistribution	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.
MultinomialDistribution.Domain	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.
int	getInputDimensionality() Gets the dimensionality of the parameters
Vector	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
int	getNumTrials() Getter for numTrials
Vector	getParameters() Getter for parameters
MultivariatePolyaDistribution.PMF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super Vector> output) Draws multiple random samples from the distribution and puts the result into the given collection.
void	setNumTrials(int numTrials) Setter for numTrials
void	setParameters(Vector parameters) Setter for parameters
java.lang.String	toString()

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

sample, 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, sample

Field Detail

DEFAULT_NUM_TRIALS

public static final int DEFAULT_NUM_TRIALS

Default number of trials, 1.

See Also:

Constant Field Values

DEFAULT_DIMENSIONALITY

public static final int DEFAULT_DIMENSIONALITY

Default dimensionality, 2.

See Also:

Constant Field Values

parameters

protected Vector parameters

Parameters of the Dirichlet distribution, must be at least 2-dimensional and each element must be positive.

Constructor Detail

MultivariatePolyaDistribution

public MultivariatePolyaDistribution()

Creates a new instance of DirichletDistribution

MultivariatePolyaDistribution

public MultivariatePolyaDistribution(int dimensionality,
                                     int numTrials)

Creates a new instance of MultivariatePolyaDistribution

Parameters:

dimensionality - Dimensionality of the distribution

numTrials - Number of trials in the distribution, must be greater than 0.

MultivariatePolyaDistribution

public MultivariatePolyaDistribution(Vector parameters,
                                     int numTrials)

Creates a new instance of MultivariatePolyaDistribution

Parameters:

parameters - Parameters of the Dirichlet distribution, must be at least 2-dimensional and each element must be positive.

numTrials - Number of trials in the distribution, must be greater than 0.

MultivariatePolyaDistribution

public MultivariatePolyaDistribution(MultivariatePolyaDistribution other)

Copy Constructor.

Parameters:

other - MultivariatePolyaDistribution to copy.

Method Detail

clone

public MultivariatePolyaDistribution 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 AbstractCloneableSerializable

Returns:

A clone of this object.

getMean

public Vector 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<Vector>

Returns:

Mean of the distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super Vector> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Specified by:

sampleInto in interface Distribution<Vector>

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.

getNumTrials

public int getNumTrials()

Getter for numTrials

Returns:

Number of trials in the distribution, must be greater than 0.

setNumTrials

public void setNumTrials(int numTrials)

Setter for numTrials

Parameters:

numTrials - Number of trials in the distribution, must be greater than 0.

getProbabilityFunction

public MultivariatePolyaDistribution.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<Vector>

Specified by:

getProbabilityFunction in interface DiscreteDistribution<Vector>

Returns:

Distribution function associated with this 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.

getInputDimensionality

public int getInputDimensionality()

Gets the dimensionality of the parameters

Returns:

Number of parameters in the distribution.

getParameters

public Vector getParameters()

Getter for parameters

Returns:

Parameters of the Dirichlet distribution, must be at least 2-dimensional and each element must be positive.

setParameters

public void setParameters(Vector parameters)

Setter for parameters

Parameters:

parameters - Parameters of the Dirichlet distribution, must be at least 2-dimensional and each element must be positive.

getDomain

public MultinomialDistribution.Domain 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<Vector>

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<Vector>

Returns:

The size of the domain.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object