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

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.PredictiveDistribution
All Implemented Interfaces:
Evaluator<Vectorizable,UnivariateGaussian.PDF>, CloneableSerializable, Serializable, 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
BayesianLinearRegression.PredictiveDistribution(MultivariateGaussian posterior)
          Creates a new instance of PredictiveDistribution
 
Method Summary
 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

BayesianLinearRegression.PredictiveDistribution

public BayesianLinearRegression.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.