gov.sandia.cognition.statistics.bayesian.conjugate

Class UniformDistributionBayesianEstimator

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,UniformDistribution,ParetoDistribution>

gov.sandia.cognition.statistics.bayesian.conjugate.UniformDistributionBayesianEstimator

All Implemented Interfaces:

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

@PublicationReference(author="Wikipedia",
                      title="Conjugate prior",
                      year=2010,
                      type=WebPage,
                      url="http://en.wikipedia.org/wiki/Conjugate_prior")
public class UniformDistributionBayesianEstimator
extends AbstractConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UniformDistribution,ParetoDistribution>

A Bayesian estimator for a conditional Uniform(0,theta) distribution using its conjugate prior Pareto 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	UniformDistributionBayesianEstimator.Parameter Parameter of this conjugate prior relationship.

Field Summary

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

parameter

Constructor Summary

Constructors

Constructor and Description
UniformDistributionBayesianEstimator() Creates a new instance of UniformDistributionBayesianEstimator
UniformDistributionBayesianEstimator(BayesianParameter<java.lang.Double,UniformDistribution,ParetoDistribution> parameter) Creates a new instance
UniformDistributionBayesianEstimator(ParetoDistribution belief) Creates a new instance of UniformDistributionBayesianEstimator
UniformDistributionBayesianEstimator(UniformDistribution conditional, ParetoDistribution prior) Creates a new instance of PoissonBayesianEstimator

Method Summary

Modifier and Type	Method and Description
double	computeEquivalentSampleSize(ParetoDistribution belief) Computes the equivalent sample size of using the given prior.
UniformDistributionBayesianEstimator.Parameter	createParameter(UniformDistribution conditional, ParetoDistribution prior) Creates a parameter linking the conditional and prior distributions
void	update(ParetoDistribution target, 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

Constructor Detail

UniformDistributionBayesianEstimator

public UniformDistributionBayesianEstimator()

Creates a new instance of UniformDistributionBayesianEstimator

UniformDistributionBayesianEstimator

public UniformDistributionBayesianEstimator(ParetoDistribution belief)

Creates a new instance of UniformDistributionBayesianEstimator

Parameters:

belief - Conjugate prior to use.

UniformDistributionBayesianEstimator

public UniformDistributionBayesianEstimator(UniformDistribution conditional,
                                            ParetoDistribution prior)

Creates a new instance of PoissonBayesianEstimator

Parameters:

prior - Default conjugate prior.

conditional - Conditional distribution of the conjugate prior.

UniformDistributionBayesianEstimator

public UniformDistributionBayesianEstimator(BayesianParameter<java.lang.Double,UniformDistribution,ParetoDistribution> parameter)

Creates a new instance

Parameters:

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

Method Detail

createParameter

public UniformDistributionBayesianEstimator.Parameter createParameter(UniformDistribution conditional,
                                                                      ParetoDistribution 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(ParetoDistribution target,
                   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:

target - The object to update.

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

computeEquivalentSampleSize

public double computeEquivalentSampleSize(ParetoDistribution 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.