@PublicationReference(author="Christopher M. Bishop", title="Pattern Recognition and Machine Learning", type=Book, year=2006, pages=308, notes="Equations 6.66 and 6.67") public class GaussianProcessRegression.PredictiveDistribution extends AbstractCloneableSerializable implements Evaluator<InputType,UnivariateGaussian>
| Constructor and Description |
|---|
PredictiveDistribution(MultivariateGaussian posterior,
java.util.ArrayList<InputType> inputs)
Creates a new instance of PredictiveDistribution
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| Modifier and Type | Method and Description |
|---|---|
UnivariateGaussian |
evaluate(InputType input)
Evaluates the function on the given input and returns the output.
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clonepublic PredictiveDistribution(MultivariateGaussian posterior, java.util.ArrayList<InputType> inputs)
posterior - Posterior distribution of the Gaussian process given the data.inputs - Inputs that we've condition on.public UnivariateGaussian evaluate(InputType input)
Evaluatorevaluate in interface Evaluator<InputType,UnivariateGaussian>input - The input to evaluate.