gov.sandia.cognition.statistics.bayesian

Interface DirichletProcessMixtureModel.Updater<ObservationType>

Type Parameters:

ObservationType - Type of observations handled by the mixture model

All Superinterfaces:

java.lang.Cloneable, CloneableSerializable, java.io.Serializable

All Known Implementing Classes:

DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater, DirichletProcessMixtureModel.MultivariateMeanUpdater

Enclosing class:

DirichletProcessMixtureModel<ObservationType>

public static interface DirichletProcessMixtureModel.Updater<ObservationType>
extends CloneableSerializable

Updater for the DPMM

Method Summary

Modifier and Type	Method and Description
ProbabilityFunction<ObservationType>	createClusterPosterior(java.lang.Iterable<? extends ObservationType> values, java.util.Random random) Updates the cluster from the values assigned to it
ProbabilityFunction<ObservationType>	createPriorPredictive(java.lang.Iterable<? extends ObservationType> data) Creates the prior predictive distribution from the data.

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

clone

Method Detail

createPriorPredictive

ProbabilityFunction<ObservationType> createPriorPredictive(java.lang.Iterable<? extends ObservationType> data)

Creates the prior predictive distribution from the data.

Parameters:

data - Data from which to create the prior predictive

Returns:

Prior predictive distribution from the data

createClusterPosterior

ProbabilityFunction<ObservationType> createClusterPosterior(java.lang.Iterable<? extends ObservationType> values,
                                                            java.util.Random random)

Updates the cluster from the values assigned to it

Parameters:

values - Values assigned to the cluster

random - Random number generator

Returns:

Updated cluster value