gov.sandia.cognition.statistics.bayesian

Class RejectionSampling<ObservationType,ParameterType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.util.AbstractRandomized

gov.sandia.cognition.statistics.bayesian.RejectionSampling<ObservationType,ParameterType>

Type Parameters:

ObservationType - Type of observation

ParameterType - Type of parameters to infer

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>, BayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author="Wikipedia",
                      title="Rejection Sampling",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Rejection_sampling")
public class RejectionSampling<ObservationType,ParameterType>
extends AbstractRandomized
implements BayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>

Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from. Typically, we sample from the parameter prior to infer the likelihood of the parameters given an observation sequence. In my limited experience, vanilla rejection sampling, implemented here, is inferior to ImportanceSamping.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	RejectionSampling.DefaultUpdater<ObservationType,ParameterType> Default ImportanceSampling Updater that uses a BayesianParameter to compute the quantities of interest.
static class	RejectionSampling.ScalarEstimator<ObservationType> Routine for estimating the minimum scalar needed to envelop the conjunctive distribution.
static interface	RejectionSampling.Updater<ObservationType,ParameterType> Updater for ImportanceSampling

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_NUM_SAMPLES Default number of samples, 1000.
protected RejectionSampling.Updater<ObservationType,ParameterType>	updater Updater for the ImportanceSampling algorithm.

Fields inherited from class gov.sandia.cognition.util.AbstractRandomized

random

Constructor Summary

Constructors

Constructor and Description
RejectionSampling() Creates a new instance of RejectionSampling

Method Summary

Modifier and Type	Method and Description
RejectionSampling<ObservationType,ParameterType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
int	getNumSamples() Getter for numSamples
RejectionSampling.Updater<ObservationType,ParameterType>	getUpdater() Getter for updater
DataDistribution<ParameterType>	learn(java.util.Collection<? extends ObservationType> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setNumSamples(int numSamples) Setter for numSamples
void	setUpdater(RejectionSampling.Updater<ObservationType,ParameterType> updater) Setter for updater

Methods inherited from class gov.sandia.cognition.util.AbstractRandomized

getRandom, setRandom

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_NUM_SAMPLES

public static final int DEFAULT_NUM_SAMPLES

Default number of samples, 1000.

See Also:

Constant Field Values

updater

protected RejectionSampling.Updater<ObservationType,ParameterType> updater

Updater for the ImportanceSampling algorithm.

Constructor Detail

RejectionSampling

public RejectionSampling()

Creates a new instance of RejectionSampling

Method Detail

clone

public RejectionSampling<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 AbstractRandomized

Returns:

A clone of this object.

learn

public DataDistribution<ParameterType> learn(java.util.Collection<? extends ObservationType> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

getNumSamples

public int getNumSamples()

Getter for numSamples

Returns:

Number of samples.

setNumSamples

public void setNumSamples(int numSamples)

Setter for numSamples

Parameters:

numSamples - Number of samples.

getUpdater

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

Getter for updater

Returns:

Updater for the ImportanceSampling algorithm.

setUpdater

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

Setter for updater

Parameters:

updater - Updater for the ImportanceSampling algorithm.