@PublicationReference(author="William M. Bolstad", title="Introduction to Bayesian Statistics: Second Edition", type=Book, year=2007, pages=208) public class MultivariateGaussianMeanBayesianEstimator extends AbstractConjugatePriorBayesianEstimator<Vector,Vector,MultivariateGaussian,MultivariateGaussian> implements ConjugatePriorBayesianEstimatorPredictor<Vector,Vector,MultivariateGaussian,MultivariateGaussian>
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static class |
MultivariateGaussianMeanBayesianEstimator.Parameter
Parameter of this conjugate prior relationship.
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| Modifier and Type | Field and Description |
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static int |
DEFAULT_DIMENSIONALITY
Default dimensionality, 1.
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parameter| Modifier | Constructor and Description |
|---|---|
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MultivariateGaussianMeanBayesianEstimator()
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
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protected |
MultivariateGaussianMeanBayesianEstimator(BayesianParameter<Vector,MultivariateGaussian,MultivariateGaussian> parameter)
Creates a new instance
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MultivariateGaussianMeanBayesianEstimator(int dimensionality)
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
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MultivariateGaussianMeanBayesianEstimator(Matrix knownCovarianceInverse)
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
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MultivariateGaussianMeanBayesianEstimator(Matrix knownCovarianceInverse,
MultivariateGaussian belief)
Creates a new instance of MultivariateGaussianMeanBayesianEstimator
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MultivariateGaussianMeanBayesianEstimator(MultivariateGaussian conditional,
MultivariateGaussian prior)
Creates a new instance of PoissonBayesianEstimator
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| Modifier and Type | Method and Description |
|---|---|
double |
computeEquivalentSampleSize(MultivariateGaussian belief)
Computes the equivalent sample size of using the given prior.
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MultivariateGaussian |
createConditionalDistribution(Vector parameter)
Creates an instance of the class conditional distribution,
parameterized by the given parameter value.
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MultivariateGaussianMeanBayesianEstimator.Parameter |
createParameter(MultivariateGaussian conditional,
MultivariateGaussian prior)
Creates a parameter linking the conditional and prior distributions
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MultivariateGaussian |
createPredictiveDistribution(MultivariateGaussian posterior)
Creates the predictive distribution of new data given the posterior.
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Matrix |
getKnownCovarianceInverse()
Getter for knownCovarianceInverse.
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void |
setKnownCovarianceInverse(Matrix knownCovarianceInverse)
Setter for knownCovarianceInverse.
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void |
update(MultivariateGaussian target,
java.lang.Iterable<? extends Vector> data)
The
update method updates an object of ResultType using
the given new Iterable containing some number of type DataType,
using some form of "learning" algorithm. |
void |
update(MultivariateGaussian updater,
Vector data)
The
update method updates an object of ResultType using
the given new data of type DataType, using some form of
"learning" algorithm. |
clone, createInitialLearnedObject, getInitialBelief, getParameter, setParameterlearn, learnequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetParameterlearnclonecreateInitialLearnedObjectpublic static final int DEFAULT_DIMENSIONALITY
public MultivariateGaussianMeanBayesianEstimator()
public MultivariateGaussianMeanBayesianEstimator(int dimensionality)
dimensionality - Dimensionality of the Vectorspublic MultivariateGaussianMeanBayesianEstimator(Matrix knownCovarianceInverse)
knownCovarianceInverse - Known covariance matrix of the estimated mean.public MultivariateGaussianMeanBayesianEstimator(Matrix knownCovarianceInverse, MultivariateGaussian belief)
knownCovarianceInverse - Known covariance matrix inverse of the estimated mean. Sometimes
called the "precision matrix".belief - Belief distribution of the mean.public MultivariateGaussianMeanBayesianEstimator(MultivariateGaussian conditional, MultivariateGaussian prior)
prior - Default conjugate prior.conditional - Conditional distribution of the conjugate prior.protected MultivariateGaussianMeanBayesianEstimator(BayesianParameter<Vector,MultivariateGaussian,MultivariateGaussian> parameter)
parameter - Bayesian hyperparameter relationship between the conditional
distribution and the conjugate prior distribution.public MultivariateGaussianMeanBayesianEstimator.Parameter createParameter(MultivariateGaussian conditional, MultivariateGaussian prior)
ConjugatePriorBayesianEstimatorcreateParameter in interface ConjugatePriorBayesianEstimator<Vector,Vector,MultivariateGaussian,MultivariateGaussian>conditional - Distribution from which observations are generatedprior - Distribution that generates parameters for the conditionalpublic Matrix getKnownCovarianceInverse()
public void setKnownCovarianceInverse(Matrix knownCovarianceInverse)
knownCovarianceInverse - Known covariance matrix inverse of the estimated mean. Sometimes
called the "precision matrix".public void update(MultivariateGaussian target, java.lang.Iterable<? extends Vector> data)
IncrementalLearnerupdate method updates an object of ResultType using
the given new Iterable containing some number of type DataType,
using some form of "learning" algorithm.update in interface IncrementalLearner<Vector,MultivariateGaussian>update in class AbstractBatchAndIncrementalLearner<Vector,MultivariateGaussian>target - The object to update.data - The Iterable containing data for the learning algorithm to use to
update the object.public void update(MultivariateGaussian updater, Vector data)
IncrementalLearnerupdate method updates an object of ResultType using
the given new data of type DataType, using some form of
"learning" algorithm.update in interface IncrementalLearner<Vector,MultivariateGaussian>updater - The object to update.data - The new data for the learning algorithm to use to update
the object.public double computeEquivalentSampleSize(MultivariateGaussian belief)
ConjugatePriorBayesianEstimatorcomputeEquivalentSampleSize in interface ConjugatePriorBayesianEstimator<Vector,Vector,MultivariateGaussian,MultivariateGaussian>belief - Prior belief to measure.public MultivariateGaussian createPredictiveDistribution(MultivariateGaussian posterior)
BayesianEstimatorPredictorcreatePredictiveDistribution in interface BayesianEstimatorPredictor<Vector,Vector,MultivariateGaussian>posterior - Posterior distribution from which to compute the predictive posterior.public MultivariateGaussian createConditionalDistribution(Vector parameter)
ConjugatePriorBayesianEstimatorcreateConditionalDistribution in interface ConjugatePriorBayesianEstimator<Vector,Vector,MultivariateGaussian,MultivariateGaussian>createConditionalDistribution in class AbstractConjugatePriorBayesianEstimator<Vector,Vector,MultivariateGaussian,MultivariateGaussian>parameter - Parameter used to create the class conditional distribution.