gov.sandia.cognition.statistics.bayesian
Class MetropolisHastingsAlgorithm<ObservationType,ParameterType>

java.lang.Object
  gov.sandia.cognition.util.AbstractCloneableSerializable
      gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm
          gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>
              gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<Collection<? extends ObservationType>,DataDistribution<ParameterType>>
                  gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>
                      gov.sandia.cognition.statistics.bayesian.MetropolisHastingsAlgorithm<ObservationType,ParameterType>

Type Parameters:

ObservationType - Type of observations handled by the MCMC algorithm.

ParameterType - Type of parameters to infer.

All Implemented Interfaces:

AnytimeAlgorithm<DataDistribution<ParameterType>>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<Collection<? extends ObservationType>,DataDistribution<ParameterType>>, BatchLearner<Collection<? extends ObservationType>,DataDistribution<ParameterType>>, BayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>, MarkovChainMonteCarlo<ObservationType,ParameterType>, CloneableSerializable, Randomized, Serializable, Cloneable

@PublicationReference(author="Wikipedia",
                      title="Metropolis\u2013Hastings algorithm",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Metropolis-Hastings_algorithm")
public class MetropolisHastingsAlgorithm<ObservationType,ParameterType>
extends AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>
implements MeasurablePerformanceAlgorithm

An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

static interface

MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> Creates proposals for the MCMC steps.

Field Summary

static String	PERFORMANCE_NAME Performance statistic name, "Current Log Likelihood".
protected MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType>	updater The object that makes proposal samples from the current location.

Fields inherited from class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

currentParameter, DEFAULT_NUM_SAMPLES, previousParameter, random

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

MetropolisHastingsAlgorithm()
Creates a new instance of MetropolisHastingsAlgorithm.

Method Summary

MetropolisHastingsAlgorithm<ObservationType,ParameterType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
ParameterType	createInitialLearnedObject() Creates the initial parameters from which to start the Markov chain.
NamedValue<Double>	getPerformance() Gets the name-value pair that describes the current performance of the algorithm.
MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType>	getUpdater() Gets the object that makes proposal samples from the current location.
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
protected void	mcmcUpdate() Performs a valid MCMC update step.
void	setUpdater(MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> updater) Sets the object that makes proposal samples from the current location.

Methods inherited from class gov.sandia.cognition.statistics.bayesian.AbstractMarkovChainMonteCarlo

cleanupAlgorithm, getBurnInIterations, getCurrentParameter, getIterationsPerSample, getPreviousParameter, getRandom, getResult, setBurnInIterations, setCurrentParameter, setIterationsPerSample, setRandom, setResult, step

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

getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

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.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid, stop

Field Detail

PERFORMANCE_NAME

public static final String PERFORMANCE_NAME

Performance statistic name, "Current Log Likelihood".

See Also:

Constant Field Values

updater

protected MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> updater

The object that makes proposal samples from the current location.

Constructor Detail

MetropolisHastingsAlgorithm

public MetropolisHastingsAlgorithm()

Creates a new instance of MetropolisHastingsAlgorithm.

Method Detail

clone

public MetropolisHastingsAlgorithm<ObservationType,ParameterType> 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 CloneableSerializable

Overrides:

clone in class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>

Returns:

A clone of this object.

initializeAlgorithm

protected boolean initializeAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.

Overrides:

initializeAlgorithm in class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>

Returns:

True if the learning algorithm can be run and false if it cannot.

mcmcUpdate

protected void mcmcUpdate()

Description copied from class: AbstractMarkovChainMonteCarlo

Performs a valid MCMC update step. That is, the function is expected to modify the currentParameter member.

Specified by:

mcmcUpdate in class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>

getPerformance

public NamedValue<Double> getPerformance()

Description copied from interface: MeasurablePerformanceAlgorithm

Gets the name-value pair that describes the current performance of the algorithm. For most algorithms, this is the value that they are attempting to optimize.

Specified by:

getPerformance in interface MeasurablePerformanceAlgorithm

Returns:

The name-value pair that describes the current performance of the algorithm.

getUpdater

public MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> getUpdater()

Gets the object that makes proposal samples from the current location.

Returns:

The object that makes proposal samples from the current location.

setUpdater

public void setUpdater(MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType> updater)

Sets the object that makes proposal samples from the current location.

Parameters:

updater - The object that makes proposal samples from the current location.

createInitialLearnedObject

public ParameterType createInitialLearnedObject()

Description copied from class: AbstractMarkovChainMonteCarlo

Creates the initial parameters from which to start the Markov chain.

Specified by:

createInitialLearnedObject in class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>

Returns:

initial parameters from which to start the Markov chain.