gov.sandia.cognition.statistics.bayesian.conjugate

Class BernoulliBayesianEstimator

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.Number,java.lang.Double,BernoulliDistribution,BetaDistribution>

gov.sandia.cognition.statistics.bayesian.conjugate.BernoulliBayesianEstimator

All Implemented Interfaces:

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

@PublicationReference(author="William M. Bolstad",
                      title="Introduction to Bayesian Statistics: Second Edition",
                      type=Book,
                      year=2007,
                      pages=143)
public class BernoulliBayesianEstimator
extends AbstractConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,BernoulliDistribution,BetaDistribution>

A Bayesian estimator for the parameter of a BernoulliDistribution using the conjugate prior BetaDistribution.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

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

Field Summary

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

parameter

Constructor Summary

Constructors

Modifier	Constructor and Description
	BernoulliBayesianEstimator() Creates a new instance of BernoulliBayesianEstimator
protected	BernoulliBayesianEstimator(BayesianParameter<java.lang.Double,BernoulliDistribution,BetaDistribution> parameter) Creates a new instance of BernoulliBayesianEstimator
	BernoulliBayesianEstimator(BernoulliDistribution conditional, BetaDistribution prior) Creates a new instance of BernoulliBayesianEstimator
	BernoulliBayesianEstimator(BetaDistribution prior) Creates a new instance of BernoulliBayesianEstimator

Method Summary

Modifier and Type	Method and Description
double	computeEquivalentSampleSize(BetaDistribution belief) Computes the equivalent sample size of using the given prior.
BernoulliBayesianEstimator.Parameter	createParameter(BernoulliDistribution conditional, BetaDistribution prior) Creates a parameter linking the conditional and prior distributions
void	update(BetaDistribution updater, java.lang.Number 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

Constructor Detail

BernoulliBayesianEstimator

public BernoulliBayesianEstimator()

Creates a new instance of BernoulliBayesianEstimator

BernoulliBayesianEstimator

public BernoulliBayesianEstimator(BetaDistribution prior)

Creates a new instance of BernoulliBayesianEstimator

Parameters:

prior - Default conjugate prior.

BernoulliBayesianEstimator

public BernoulliBayesianEstimator(BernoulliDistribution conditional,
                                  BetaDistribution prior)

Creates a new instance of BernoulliBayesianEstimator

Parameters:

prior - Default conjugate prior.

conditional - Conditional distribution of the conjugate prior.

BernoulliBayesianEstimator

protected BernoulliBayesianEstimator(BayesianParameter<java.lang.Double,BernoulliDistribution,BetaDistribution> parameter)

Creates a new instance of BernoulliBayesianEstimator

Parameters:

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

Method Detail

createParameter

public BernoulliBayesianEstimator.Parameter createParameter(BernoulliDistribution conditional,
                                                            BetaDistribution 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

update

public void update(BetaDistribution updater,
                   java.lang.Number 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:

updater - The object to update.

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

computeEquivalentSampleSize

public double computeEquivalentSampleSize(BetaDistribution 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.