gov.sandia.cognition.statistics.distribution

Class InverseGammaDistribution

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.InverseGammaDistribution

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>, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

InverseGammaDistribution.CDF, InverseGammaDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Inverse-gamma distribution",
                      type=WebPage,
                      year=2010,
                      url="http://en.wikipedia.org/wiki/Inverse-gamma_distribution")
public class InverseGammaDistribution
extends AbstractClosedFormSmoothUnivariateDistribution

Defines an inverse-gamma distribution. That is, if X is drawn from IG(a,b), then 1/X follows a Gamma(a,1.0/b) 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	InverseGammaDistribution.CDF CDF of the inverseRootFinder-gamma distribution.
static class	InverseGammaDistribution.PDF PDF of the inverseRootFinder-Gamma distribution.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_SCALE Default scale, 1.0.
static double	DEFAULT_SHAPE Default shape, 3.0.
protected double	scale Scale parameter, must be greater than zero.
protected double	shape Shape parameter, must be greater than zero.

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
InverseGammaDistribution	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.
InverseGammaDistribution.CDF	getCDF() Gets the CDF of a scalar 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.
InverseGammaDistribution.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
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	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.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, wait, wait, wait

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

sample, sample

Field Detail

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

shape

protected double shape

Shape parameter, must be greater than zero.

scale

protected double scale

Scale parameter, must be greater than zero.

Constructor Detail

InverseGammaDistribution

public InverseGammaDistribution()

Creates a new instance of InverseGammaDistribution

InverseGammaDistribution

public InverseGammaDistribution(double shape,
                                double scale)

Creates a new instance of InverseGammaDistribution

Parameters:

shape - Shape parameter, must be greater than zero.

scale - Scale parameter, must be greater than zero.

InverseGammaDistribution

public InverseGammaDistribution(InverseGammaDistribution other)

Copy constructor

Parameters:

other - InverseGammaDistribution to copy

Method Detail

clone

public InverseGammaDistribution 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.

getShape

public double getShape()

Getter for shape

Returns:

Shape parameter, must be greater than zero.

setShape

public void setShape(double shape)

Setter for shape

Parameters:

shape - Shape parameter, must be greater than zero.

getScale

public double getScale()

Getter for scale

Returns:

Scale parameter, must be greater than zero.

setScale

public void setScale(double scale)

Setter for scale

Parameters:

scale - the scale to set Scale parameter, must be greater than zero.

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.

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()

Description copied from interface: Vectorizable

Converts the object to a vector.

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.

Parameters:

parameters - The parameters to incorporate.

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a 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.

Returns:

Variance of the distribution.

getCDF

public InverseGammaDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Returns:

CDF of the scalar distribution.

getProbabilityFunction

public InverseGammaDistribution.PDF getProbabilityFunction()

Description copied from interface: ComputableDistribution

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

Returns:

Distribution function associated with this Distribution.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getMinSupport

public java.lang.Double getMinSupport()

Description copied from interface: UnivariateDistribution

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

Returns:

Minimum support.

getMaxSupport

public java.lang.Double getMaxSupport()

Description copied from interface: UnivariateDistribution

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

Returns:

Minimum support.