gov.sandia.cognition.statistics.bayesian.conjugate

Class GammaInverseScaleBayesianEstimator

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<ObservationType,BeliefType>

gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,GammaDistribution,GammaDistribution>

gov.sandia.cognition.statistics.bayesian.conjugate.GammaInverseScaleBayesianEstimator

All Implemented Interfaces:

BatchAndIncrementalLearner<java.lang.Double,GammaDistribution>, BatchLearner<java.util.Collection<? extends java.lang.Double>,GammaDistribution>, IncrementalLearner<java.lang.Double,GammaDistribution>, BayesianEstimator<java.lang.Double,java.lang.Double,GammaDistribution>, ConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,GammaDistribution,GammaDistribution>, RecursiveBayesianEstimator<java.lang.Double,java.lang.Double,GammaDistribution>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class GammaInverseScaleBayesianEstimator
extends AbstractConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,GammaDistribution,GammaDistribution>

A Bayesian estimator for the scale parameter of a Gamma distribution using the conjugate prior Gamma distribution for the inverse-scale (rate) of the Gamma.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	GammaInverseScaleBayesianEstimator.Parameter Bayesian parameter describing this conjugate relationship.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_SHAPE Default shape, 1.0.

Fields inherited from class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator

parameter

Constructor Summary

Constructors

Modifier	Constructor and Description
	GammaInverseScaleBayesianEstimator() Creates a new instance of GammaInverseScaleBayesianEstimator
protected	GammaInverseScaleBayesianEstimator(BayesianParameter<java.lang.Double,GammaDistribution,GammaDistribution> parameter) Creates a new instance of GammaInverseScaleBayesianEstimator
	GammaInverseScaleBayesianEstimator(double shape, GammaDistribution prior) Creates a new instance of GammaInverseScaleBayesianEstimator
	GammaInverseScaleBayesianEstimator(GammaDistribution conditional, GammaDistribution prior) Creates a new instance of GammaInverseScaleBayesianEstimator

Method Summary

Modifier and Type	Method and Description
double	computeEquivalentSampleSize(GammaDistribution belief) Computes the equivalent sample size of using the given prior.
GammaInverseScaleBayesianEstimator.Parameter	createParameter(GammaDistribution conditional, GammaDistribution prior) Creates a parameter linking the conditional and prior distributions
double	getShape() Gets the shape of the conditional distribution
void	setShape(double shape) Sets the shape of the conditional distribution
void	update(GammaDistribution belief, java.lang.Double data) The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

Methods inherited from class gov.sandia.cognition.statistics.bayesian.conjugate.AbstractConjugatePriorBayesianEstimator

clone, createConditionalDistribution, createInitialLearnedObject, getInitialBelief, getParameter, setParameter

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner

learn, learn, update

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.learning.algorithm.IncrementalLearner

update

Field Detail

DEFAULT_SHAPE

public static final double DEFAULT_SHAPE

Default shape, 1.0.

See Also:

Constant Field Values

Constructor Detail

GammaInverseScaleBayesianEstimator

public GammaInverseScaleBayesianEstimator()

Creates a new instance of GammaInverseScaleBayesianEstimator

GammaInverseScaleBayesianEstimator

public GammaInverseScaleBayesianEstimator(double shape,
                                          GammaDistribution prior)

Creates a new instance of GammaInverseScaleBayesianEstimator

Parameters:

shape - Shape of the conditional distribution

prior - Default conjugate prior.

GammaInverseScaleBayesianEstimator

public GammaInverseScaleBayesianEstimator(GammaDistribution conditional,
                                          GammaDistribution prior)

Creates a new instance of GammaInverseScaleBayesianEstimator

Parameters:

prior - Default conjugate prior.

conditional - Conditional distribution of the conjugate prior.

GammaInverseScaleBayesianEstimator

protected GammaInverseScaleBayesianEstimator(BayesianParameter<java.lang.Double,GammaDistribution,GammaDistribution> parameter)

Creates a new instance of GammaInverseScaleBayesianEstimator

Parameters:

parameter - Bayesian parameter describing this conjugate relationship.

Method Detail

createParameter

public GammaInverseScaleBayesianEstimator.Parameter createParameter(GammaDistribution conditional,
                                                                    GammaDistribution prior)

Description copied from interface: ConjugatePriorBayesianEstimator

Creates a parameter linking the conditional and prior distributions

Parameters:

conditional - Distribution from which observations are generated

prior - Distribution that generates parameters for the conditional

Returns:

Parameter describing the relationship between the conditional and prior

getShape

public double getShape()

Gets the shape of the conditional distribution

Returns:

Shape of the conditional distribution

setShape

public void setShape(double shape)

Sets the shape of the conditional distribution

Parameters:

shape - Shape of the conditional distribution

update

public void update(GammaDistribution belief,
                   java.lang.Double data)

Description copied from interface: IncrementalLearner

The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

Parameters:

belief - The object to update.

data - The new data for the learning algorithm to use to update the object.

computeEquivalentSampleSize

public double computeEquivalentSampleSize(GammaDistribution belief)

Description copied from interface: ConjugatePriorBayesianEstimator

Computes the equivalent sample size of using the given prior. This is effectively how many samples of bias the prior injects into the estimate.

Parameters:

belief - Prior belief to measure.

Returns:

Equivalent sample size of the initial belief.