- Type Parameters:
ObservationType - Type of observations incorporated by the modelParameterType - Type of parameter that we are estimatingBeliefType - Belief distribution of the parameter
- All Superinterfaces:
- BatchLearner<java.util.Collection<? extends ObservationType>,BeliefType>, BayesianEstimator<ObservationType,ParameterType,BeliefType>, java.lang.Cloneable, CloneableSerializable, IncrementalLearner<ObservationType,BeliefType>, java.io.Serializable
- All Known Subinterfaces:
- ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType,BeliefType>, ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType,BeliefType>, ParticleFilter<ObservationType,ParameterType>
- All Known Implementing Classes:
- AbstractConjugatePriorBayesianEstimator, AbstractKalmanFilter, AbstractParticleFilter, BernoulliBayesianEstimator, BinomialBayesianEstimator, ExponentialBayesianEstimator, ExtendedKalmanFilter, GammaInverseScaleBayesianEstimator, KalmanFilter, MultinomialBayesianEstimator, MultivariateGaussianMeanBayesianEstimator, MultivariateGaussianMeanCovarianceBayesianEstimator, PoissonBayesianEstimator, SamplingImportanceResamplingParticleFilter, UniformDistributionBayesianEstimator, UnivariateGaussianMeanBayesianEstimator, UnivariateGaussianMeanVarianceBayesianEstimator
@PublicationReference(author="Wikipedia",
title="Recursive Bayesian estimation",
type=WebPage,
year=2010,
url="http://en.wikipedia.org/wiki/Recursive_Bayesian_estimation")
public interface RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>>
extends BayesianEstimator<ObservationType,ParameterType,BeliefType>, IncrementalLearner<ObservationType,BeliefType>
A recursive Bayesian estimator is an estimation method that uses the
previous belief of the system parameter and a single observation to refine
the estimate of the system parameter.
- Since:
- 3.0
- Author:
- Kevin R. Dixon