@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 |
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PredictiveDistribution(MultivariateGaussian posterior,
java.util.ArrayList<InputType> inputs)
Creates a new instance of PredictiveDistribution
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Modifier and Type | Method and Description |
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UnivariateGaussian |
evaluate(InputType input)
Evaluates the function on the given input and returns the output.
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clone
public 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)
Evaluator
evaluate
in interface Evaluator<InputType,UnivariateGaussian>
input
- The input to evaluate.