@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 |
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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 |
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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, setParameter
learn, learn
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
createConditionalDistribution, getParameter
learn
clone
createInitialLearnedObject
public 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)
ConjugatePriorBayesianEstimator
createParameter
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)
IncrementalLearner
update
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)
IncrementalLearner
update
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)
ConjugatePriorBayesianEstimator
computeEquivalentSampleSize
in interface ConjugatePriorBayesianEstimator<Vector,Matrix,MultivariateGaussian,NormalInverseWishartDistribution>
belief
- Prior belief to measure.public MultivariateStudentTDistribution createPredictiveDistribution(NormalInverseWishartDistribution posterior)
BayesianEstimatorPredictor
createPredictiveDistribution
in interface BayesianEstimatorPredictor<Vector,Matrix,NormalInverseWishartDistribution>
posterior
- Posterior distribution from which to compute the predictive posterior.