- 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<java.util.Collection<? extends ObservationType>,PosteriorType>, java.lang.Cloneable, CloneableSerializable, java.io.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
@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<java.util.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