gov.sandia.cognition.statistics.bayesian
Interface BayesianEstimatorPredictor<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>, BayesianEstimator<ObservationType,ParameterType,PosteriorType>, Cloneable, CloneableSerializable, Serializable
All Known Subinterfaces:
ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType,BeliefType>
All Known Implementing Classes:
BinomialBayesianEstimator, ExponentialBayesianEstimator, MultinomialBayesianEstimator, MultivariateGaussianMeanBayesianEstimator, MultivariateGaussianMeanCovarianceBayesianEstimator, PoissonBayesianEstimator, UnivariateGaussianMeanBayesianEstimator, UnivariateGaussianMeanVarianceBayesianEstimator

public interface BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>
extends BayesianEstimator<ObservationType,ParameterType,PosteriorType>

A BayesianEstimator that can also compute the predictive distribution of new data given the posterior.

Since:
3.0
Author:
Kevin R. Dixon

Method Summary
 ComputableDistribution<ObservationType> createPredictiveDistribution(PosteriorType posterior)
          Creates the predictive distribution of new data given the posterior.
 
Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner
learn
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Method Detail

createPredictiveDistribution

ComputableDistribution<ObservationType> createPredictiveDistribution(PosteriorType posterior)
Creates the predictive distribution of new data given the posterior. This is equivalent to evaluating the integral of: p( newdata | data ) = integral( conditional( newdata | data, parameters ) * p( parameters | data ) dparameters )

Parameters:
posterior - Posterior distribution from which to compute the predictive posterior.
Returns:
Predictive distribution of new data given the observed data.