gov.sandia.cognition.statistics.bayesian.conjugate

Class UnivariateGaussianMeanBayesianEstimator

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,UnivariateGaussian,UnivariateGaussian>

gov.sandia.cognition.statistics.bayesian.conjugate.UnivariateGaussianMeanBayesianEstimator

All Implemented Interfaces:

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

@PublicationReference(author="William M. Bolstad",
                      title="Introduction to Bayesian Statistics: Second Edition",
                      type=Book,
                      year=2007,
                      pages=208)
public class UnivariateGaussianMeanBayesianEstimator
extends AbstractConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>
implements ConjugatePriorBayesianEstimatorPredictor<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>

Bayesian estimator for the mean of a UnivariateGaussian using its conjugate prior, which is also a UnivariateGaussian. This estimation method assumes that the variance of the estimated mean is known.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	UnivariateGaussianMeanBayesianEstimator.Parameter Parameter of this conjugate prior relationship.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_KNOWN_VARIANCE Default known variance of the estimated distribution, 1.0.

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

parameter

Constructor Summary

Constructors

Modifier	Constructor and Description
	UnivariateGaussianMeanBayesianEstimator() Creates a new instance of UnivariateGaussianMeanBayesianEstimator
protected	UnivariateGaussianMeanBayesianEstimator(BayesianParameter<java.lang.Double,UnivariateGaussian,UnivariateGaussian> parameter) Creates a new instance
	UnivariateGaussianMeanBayesianEstimator(double knownVariance) Creates a new instance of UnivariateGaussianMeanBayesianEstimator
	UnivariateGaussianMeanBayesianEstimator(double knownVariance, UnivariateGaussian belief) Creates a new instance of UnivariateGaussianMeanBayesianEstimator
	UnivariateGaussianMeanBayesianEstimator(UnivariateGaussian conditional, UnivariateGaussian prior) Creates a new instance

Method Summary

Modifier and Type	Method and Description
double	computeEquivalentSampleSize(UnivariateGaussian belief) Computes the equivalent sample size of using the given prior.
UnivariateGaussianMeanBayesianEstimator.Parameter	createParameter(UnivariateGaussian conditional, UnivariateGaussian prior) Creates a parameter linking the conditional and prior distributions
UnivariateGaussian	createPredictiveDistribution(UnivariateGaussian posterior) Creates the predictive distribution from the given posterior.
double	getKnownVariance() Getter for knownVariance.
void	setKnownVariance(double knownVariance) Setter for knownVariance.
void	update(UnivariateGaussian updater, 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.statistics.bayesian.conjugate.ConjugatePriorBayesianEstimator

createConditionalDistribution, getParameter

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

learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

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

createInitialLearnedObject, update

Field Detail

DEFAULT_KNOWN_VARIANCE

public static final double DEFAULT_KNOWN_VARIANCE

Default known variance of the estimated distribution, 1.0.

See Also:

Constant Field Values

Constructor Detail

UnivariateGaussianMeanBayesianEstimator

public UnivariateGaussianMeanBayesianEstimator()

Creates a new instance of UnivariateGaussianMeanBayesianEstimator

UnivariateGaussianMeanBayesianEstimator

public UnivariateGaussianMeanBayesianEstimator(double knownVariance)

Creates a new instance of UnivariateGaussianMeanBayesianEstimator

Parameters:

knownVariance - Known variance of the distribution.

UnivariateGaussianMeanBayesianEstimator

public UnivariateGaussianMeanBayesianEstimator(double knownVariance,
                                               UnivariateGaussian belief)

Creates a new instance of UnivariateGaussianMeanBayesianEstimator

Parameters:

belief - Conjugate prior of the posterior belief.

knownVariance - Known variance of the distribution.

UnivariateGaussianMeanBayesianEstimator

public UnivariateGaussianMeanBayesianEstimator(UnivariateGaussian conditional,
                                               UnivariateGaussian prior)

Creates a new instance

Parameters:

conditional - Distribution from which observations are generated

prior - Conjugate prior to the conditional distribution

UnivariateGaussianMeanBayesianEstimator

protected UnivariateGaussianMeanBayesianEstimator(BayesianParameter<java.lang.Double,UnivariateGaussian,UnivariateGaussian> parameter)

Creates a new instance

Parameters:

parameter - Bayesian hyperparameter relationship between the conditional distribution and the conjugate prior distribution.

Method Detail

createParameter

public UnivariateGaussianMeanBayesianEstimator.Parameter createParameter(UnivariateGaussian conditional,
                                                                         UnivariateGaussian prior)

Description copied from interface: ConjugatePriorBayesianEstimator

Creates a parameter linking the conditional and prior distributions

Specified by:

createParameter in interface ConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>

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

getKnownVariance

public double getKnownVariance()

Getter for knownVariance.

Returns:

Known variance of the distribution.

setKnownVariance

public void setKnownVariance(double knownVariance)

Setter for knownVariance.

Parameters:

knownVariance - Known variance of the distribution.

update

public void update(UnivariateGaussian updater,
                   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.

Specified by:

update in interface IncrementalLearner<java.lang.Double,UnivariateGaussian>

Parameters:

updater - The object to update.

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

computeEquivalentSampleSize

public double computeEquivalentSampleSize(UnivariateGaussian 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.

Specified by:

computeEquivalentSampleSize in interface ConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>

Parameters:

belief - Prior belief to measure.

Returns:

Equivalent sample size of the initial belief.

createPredictiveDistribution

public UnivariateGaussian createPredictiveDistribution(UnivariateGaussian posterior)

Creates the predictive distribution from the given posterior.

Specified by:

createPredictiveDistribution in interface BayesianEstimatorPredictor<java.lang.Double,java.lang.Double,UnivariateGaussian>

Parameters:

posterior - Posterior from which to create the predictive distribution

Returns:

Predictive distribution from the posterior.