gov.sandia.cognition.statistics.bayesian

Interface BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>>

Type Parameters:

OutputType - Type of outputs to consider, typically a Double

PosteriorType - Posterior distribution of the weights given the observed InputOutputPairs

All Superinterfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>>,PosteriorType>, BayesianEstimator<InputOutputPair<? extends Vectorizable,OutputType>,Vector,PosteriorType>, java.lang.Cloneable, CloneableSerializable, java.io.Serializable

All Known Implementing Classes:

BayesianLinearRegression, BayesianLinearRegression.IncrementalEstimator, BayesianRobustLinearRegression, BayesianRobustLinearRegression.IncrementalEstimator

@PublicationReference(author="Christopher M. Bishop",title="Pattern Recognition and Machine Learning",type=Book,year=2006,pages={152,159}) @PublicationReference(author="Hanna M. Wallach",title="Introduction to Gaussian Process Regression",type=Misc,year=2005,url="http://www.cs.umass.edu/~wallach/talks/gp_intro.pdf") @PublicationReference(author="Wikipedia",title="Bayesian linear regression",type=WebPage,year=2010,url="http://en.wikipedia.org/wiki/Bayesian_linear_regression")
public interface BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>>
extends BayesianEstimator<InputOutputPair<? extends Vectorizable,OutputType>,Vector,PosteriorType>

A type of regression algorithm maps a Vector space, and the weights of this Vector space are represented as a posterior distribution given the observed InputOutputPairs. The system can also estimate the predictive distribution of future data given the weight posterior for a desired input value.

Since:

3.0

Author:

Kevin R. Dixon

Method Summary

Modifier and Type	Method and Description
Distribution<OutputType>	createConditionalDistribution(Vectorizable input, Vector weights) Creates the distribution from which the outputs are generated, given the weights and the input to consider.
Evaluator<? super Vectorizable,? extends ClosedFormDistribution<OutputType>>	createPredictiveDistribution(PosteriorType posterior) Creates the predictive distribution of outputs given the weight posterior

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

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

clone

Method Detail

createConditionalDistribution

Distribution<OutputType> createConditionalDistribution(Vectorizable input,
                                                       Vector weights)

Creates the distribution from which the outputs are generated, given the weights and the input to consider.

Parameters:

input - Input to condition on

weights - Weights that determine the mean

Returns:

Conditional distribution from which outputs are generated.

createPredictiveDistribution

Evaluator<? super Vectorizable,? extends ClosedFormDistribution<OutputType>> createPredictiveDistribution(PosteriorType posterior)

Creates the predictive distribution of outputs given the weight posterior

Parameters:

posterior - Posterior distribution of weights.

Returns:

Predictive distribution of outputs given the posterior.