gov.sandia.cognition.statistics.distribution

Class NormalInverseGammaDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

gov.sandia.cognition.statistics.distribution.NormalInverseGammaDistribution

All Implemented Interfaces:

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

Direct Known Subclasses:

NormalInverseGammaDistribution.PDF

@PublicationReference(author="Christopher M. Bishop",title="Pattern Recognition and Machine Learning",type=Book,year=2006,pages=101) @PublicationReference(author="Wikipedia",title="Normal-scaled inverse gamma distribution",type=WebPage,year=2010,url="http://en.wikipedia.org/wiki/Normal-scaled_inverse_gamma_distribution")
public class NormalInverseGammaDistribution
extends AbstractDistribution<Vector>
implements ClosedFormComputableDistribution<Vector>

The normal inverse-gamma distribution is the product of a univariate Gaussian distribution with an inverse-gamma distribution. It is the conjugate prior of a univariate Gaussian with unknown mean and unknown variance. (As far as I know, it has no other purpose.)

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NormalInverseGammaDistribution.PDF PDF of the NormalInverseGammaDistribution

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_LOCATION Default location, 0.0.
static double	DEFAULT_PRECISION Default precision, 1.0.
static double	DEFAULT_SCALE Default scale, 1.0.
static double	DEFAULT_SHAPE Default shape, 3.0.

Constructor Summary

Constructors

Constructor and Description
NormalInverseGammaDistribution() Creates a new instance of NormalInverseGammaDistribution
NormalInverseGammaDistribution(double location, double precision, double shape, double scale) Creates a new instance of NormalInverseGammaDistribution
NormalInverseGammaDistribution(NormalInverseGammaDistribution other) Copy constructor

Method Summary

Modifier and Type	Method and Description
NormalInverseGammaDistribution	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.
double	getLocation() Getter for location.
Vector	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
double	getPrecision() Getter for precision
NormalInverseGammaDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getScale() Getter for scale
double	getShape() Getter for shape
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	setLocation(double location) Setter for location.
void	setPrecision(double precision) Setter for precision.
void	setScale(double scale) Setter for scale
void	setShape(double shape) Setter for shape
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_LOCATION

public static final double DEFAULT_LOCATION

Default location, 0.0.

See Also:

Constant Field Values

DEFAULT_PRECISION

public static final double DEFAULT_PRECISION

Default precision, 1.0.

See Also:

Constant Field Values

DEFAULT_SHAPE

public static final double DEFAULT_SHAPE

Default shape, 3.0.

See Also:

Constant Field Values

DEFAULT_SCALE

public static final double DEFAULT_SCALE

Default scale, 1.0.

See Also:

Constant Field Values

Constructor Detail

NormalInverseGammaDistribution

public NormalInverseGammaDistribution()

Creates a new instance of NormalInverseGammaDistribution

NormalInverseGammaDistribution

public NormalInverseGammaDistribution(double location,
                                      double precision,
                                      double shape,
                                      double scale)

Creates a new instance of NormalInverseGammaDistribution

Parameters:

location - Location of the Gaussian kernel.

precision - Precision of the Gaussian kernel, must be greater than zero.

shape - Shape parameter of the Inverse Gamma kernel, must be greater than zero.

scale - Scale parameter of the Inverse Gamma kernel, must be greater than zero.

NormalInverseGammaDistribution

public NormalInverseGammaDistribution(NormalInverseGammaDistribution other)

Copy constructor

Parameters:

other - NormalInverseGammaDistribution to copy

Method Detail

clone

public NormalInverseGammaDistribution 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.

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.

getProbabilityFunction

public NormalInverseGammaDistribution.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<Vector>

Returns:

Distribution function associated with this Distribution.

getLocation

public double getLocation()

Getter for location.

Returns:

Location of the Gaussian kernel.

setLocation

public void setLocation(double location)

Setter for location.

Parameters:

location - Location of the Gaussian kernel.

getPrecision

public double getPrecision()

Getter for precision

Returns:

Precision of the Gaussian kernel, must be greater than zero.

setPrecision

public void setPrecision(double precision)

Setter for precision.

Parameters:

precision - Precision of the Gaussian kernel, must be greater than zero.

getShape

public double getShape()

Getter for shape

Returns:

Shape parameter of the Inverse Gamma kernel, must be greater than zero.

setShape

public void setShape(double shape)

Setter for shape

Parameters:

shape - Shape parameter of the Inverse Gamma kernel, must be greater than zero.

getScale

public double getScale()

Getter for scale

Returns:

Scale parameter of the Inverse Gamma kernel, must be greater than zero.

setScale

public void setScale(double scale)

Setter for scale

Parameters:

scale - Scale parameter of the Inverse Gamma kernel, must be greater than zero.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object