gov.sandia.cognition.statistics.bayesian
Interface BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>

Type Parameters:
ObservationType - Observations from the ConditionalType that are used to estimate the parameters of the distribution.
ParameterType - Type of parameter estimated by this algorithm, which is used to parameterize the conditional distribution.
PosteriorType - Type of posterior Distribution, which describes the uncertainty of the parameters after we have incorporated the observations.
All Superinterfaces:
BatchLearner<Collection<? extends ObservationType>,PosteriorType>, Cloneable, CloneableSerializable, Serializable
All Known Subinterfaces:
BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType>, BayesianRegression<OutputType,PosteriorType>, ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType,BeliefType>, ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType,BeliefType>, MarkovChainMonteCarlo<ObservationType,ParameterType>, ParticleFilter<ObservationType,ParameterType>, RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType>
All Known Implementing Classes:
AbstractConjugatePriorBayesianEstimator, AbstractKalmanFilter, AbstractMarkovChainMonteCarlo, AbstractParticleFilter, BayesianLinearRegression, BayesianLinearRegression.IncrementalEstimator, BayesianRobustLinearRegression, BayesianRobustLinearRegression.IncrementalEstimator, BernoulliBayesianEstimator, BinomialBayesianEstimator, DirichletProcessMixtureModel, ExponentialBayesianEstimator, ExtendedKalmanFilter, GammaInverseScaleBayesianEstimator, GaussianProcessRegression, ImportanceSampling, KalmanFilter, MetropolisHastingsAlgorithm, MultinomialBayesianEstimator, MultivariateGaussianMeanBayesianEstimator, MultivariateGaussianMeanCovarianceBayesianEstimator, ParallelDirichletProcessMixtureModel, PoissonBayesianEstimator, RejectionSampling, SamplingImportanceResamplingParticleFilter, UniformDistributionBayesianEstimator, UnivariateGaussianMeanBayesianEstimator, UnivariateGaussianMeanVarianceBayesianEstimator

@PublicationReferences(references={@PublicationReference(author="William M. Bolstad",title="Introduction to Bayesian Statistics: Second Edition",type=Book,year=2007,notes="Good introductory text."),@PublicationReference(author="Christian P. Robert",title="The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, Second Edition",type=Book,year=2007,notes="Good advanced text."),@PublicationReference(author="Wikipedia",title="Bayes estimator",type=WebPage,year=2009,url="http://en.wikipedia.org/wiki/Bayes_estimator")})
public interface BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>
extends BatchLearner<Collection<? extends ObservationType>,PosteriorType>

A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.

Since:
3.0
Author:
Kevin R. Dixon

Method Summary
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner
learn
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone