@PublicationReference(author={"Andrew Gelman","John B. Carlin","Hal S. Stern","Donald B. Rubin"},title="Bayesian Data Analysis, Second Edition",type=Book,year=2004,pages={87,88}) @PublicationReference(author="Wikipedia",title="Conjugate Prior",type=WebPage,year=2009,url="http://en.wikipedia.org/wiki/Conjugate_prior") public class MultivariateGaussianMeanCovarianceBayesianEstimator extends AbstractConjugatePriorBayesianEstimator<Vector,Matrix,MultivariateGaussian,NormalInverseWishartDistribution> implements ConjugatePriorBayesianEstimatorPredictor<Vector,Matrix,MultivariateGaussian,NormalInverseWishartDistribution>
| Modifier and Type | Class and Description |
|---|---|
static class |
MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter
Parameter for this conjugate prior estimator.
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parameter| Modifier | Constructor and Description |
|---|---|
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MultivariateGaussianMeanCovarianceBayesianEstimator()
Creates a new instance of MultivariateGaussianMeanCovarianceBayesianEstimator
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protected |
MultivariateGaussianMeanCovarianceBayesianEstimator(BayesianParameter<Matrix,MultivariateGaussian,NormalInverseWishartDistribution> parameter)
Creates a new instance
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MultivariateGaussianMeanCovarianceBayesianEstimator(int dimensionality)
Creates a new instance of MultivariateGaussianMeanCovarianceBayesianEstimator
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MultivariateGaussianMeanCovarianceBayesianEstimator(MultivariateGaussian conditional,
NormalInverseWishartDistribution prior)
Creates a new instance
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MultivariateGaussianMeanCovarianceBayesianEstimator(NormalInverseWishartDistribution belief)
Creates a new instance of MultivariateGaussianMeanCovarianceBayesianEstimator
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| Modifier and Type | Method and Description |
|---|---|
double |
computeEquivalentSampleSize(NormalInverseWishartDistribution belief)
Computes the equivalent sample size of using the given prior.
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MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter |
createParameter(MultivariateGaussian conditional,
NormalInverseWishartDistribution prior)
Creates a parameter linking the conditional and prior distributions
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MultivariateStudentTDistribution |
createPredictiveDistribution(NormalInverseWishartDistribution posterior)
Creates the predictive distribution of new data given the posterior.
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void |
update(NormalInverseWishartDistribution prior,
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(NormalInverseWishartDistribution target,
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, createConditionalDistribution, createInitialLearnedObject, getInitialBelief, getParameter, setParameterlearn, learnequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcreateConditionalDistribution, getParameterlearnclonecreateInitialLearnedObjectpublic MultivariateGaussianMeanCovarianceBayesianEstimator()
public MultivariateGaussianMeanCovarianceBayesianEstimator(int dimensionality)
dimensionality - Dimensionality of the observations to consider.public MultivariateGaussianMeanCovarianceBayesianEstimator(NormalInverseWishartDistribution belief)
belief - Initial belief of the conditional parameterspublic MultivariateGaussianMeanCovarianceBayesianEstimator(MultivariateGaussian conditional, NormalInverseWishartDistribution prior)
prior - Default conjugate prior.conditional - Conditional distribution of the conjugate prior.protected MultivariateGaussianMeanCovarianceBayesianEstimator(BayesianParameter<Matrix,MultivariateGaussian,NormalInverseWishartDistribution> parameter)
parameter - Bayesian hyperparameter relationship between the conditional
distribution and the conjugate prior distribution.public MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter createParameter(MultivariateGaussian conditional, NormalInverseWishartDistribution prior)
ConjugatePriorBayesianEstimatorcreateParameter in interface ConjugatePriorBayesianEstimator<Vector,Matrix,MultivariateGaussian,NormalInverseWishartDistribution>conditional - Distribution from which observations are generatedprior - Distribution that generates parameters for the conditionalpublic void update(NormalInverseWishartDistribution target, 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,NormalInverseWishartDistribution>target - The object to update.data - The new data for the learning algorithm to use to update
the object.public void update(NormalInverseWishartDistribution prior, 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,NormalInverseWishartDistribution>update in class AbstractBatchAndIncrementalLearner<Vector,NormalInverseWishartDistribution>prior - The object to update.data - The Iterable containing data for the learning algorithm to use to
update the object.public double computeEquivalentSampleSize(NormalInverseWishartDistribution belief)
ConjugatePriorBayesianEstimatorcomputeEquivalentSampleSize in interface ConjugatePriorBayesianEstimator<Vector,Matrix,MultivariateGaussian,NormalInverseWishartDistribution>belief - Prior belief to measure.public MultivariateStudentTDistribution createPredictiveDistribution(NormalInverseWishartDistribution posterior)
BayesianEstimatorPredictorcreatePredictiveDistribution in interface BayesianEstimatorPredictor<Vector,Matrix,NormalInverseWishartDistribution>posterior - Posterior distribution from which to compute the predictive posterior.