gov.sandia.cognition.statistics.bayesian

Class BayesianLinearRegression.PredictiveDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.PredictiveDistribution

All Implemented Interfaces:

Evaluator<Vectorizable,UnivariateGaussian.PDF>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

BayesianLinearRegression

@PublicationReference(author="Christopher M. Bishop",
                      title="Pattern Recognition and Machine Learning",
                      type=Book,
                      year=2006,
                      pages=156)
public class BayesianLinearRegression.PredictiveDistribution
extends AbstractCloneableSerializable
implements Evaluator<Vectorizable,UnivariateGaussian.PDF>

Creates the predictive distribution for the likelihood of a given point.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PredictiveDistribution(MultivariateGaussian posterior) Creates a new instance of PredictiveDistribution

Method Summary

Modifier and Type	Method and Description
UnivariateGaussian.PDF	evaluate(Vectorizable input) Evaluates the function on the given input and returns the output.

Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

clone

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PredictiveDistribution

public PredictiveDistribution(MultivariateGaussian posterior)

Creates a new instance of PredictiveDistribution

Parameters:

posterior - Posterior distribution of the weights given the data.

Method Detail

evaluate

public UnivariateGaussian.PDF evaluate(Vectorizable input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<Vectorizable,UnivariateGaussian.PDF>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.