@PublicationReference(author="Jeff Grynaviski",title="Bayesian Analysis of the Normal Distribution, Part II",type=Misc,year=2009,url="http://home.uchicago.edu/~grynav/bayes/ABSLec8.ppt") @PublicationReference(author="Wikipedia",title="Conjugate Prior",type=WebPage,year=2009,url="http://en.wikipedia.org/wiki/Conjugate_prior") public class UnivariateGaussianMeanVarianceBayesianEstimator extends AbstractConjugatePriorBayesianEstimator<java.lang.Double,Vector,UnivariateGaussian,NormalInverseGammaDistribution> implements ConjugatePriorBayesianEstimatorPredictor<java.lang.Double,Vector,UnivariateGaussian,NormalInverseGammaDistribution>
| Modifier and Type | Class and Description |
|---|---|
static class |
UnivariateGaussianMeanVarianceBayesianEstimator.Parameter
Parameter for this conjugate prior estimator.
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parameter| Modifier | Constructor and Description |
|---|---|
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UnivariateGaussianMeanVarianceBayesianEstimator()
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
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protected |
UnivariateGaussianMeanVarianceBayesianEstimator(BayesianParameter<Vector,UnivariateGaussian,NormalInverseGammaDistribution> parameter)
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
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UnivariateGaussianMeanVarianceBayesianEstimator(NormalInverseGammaDistribution prior)
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
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UnivariateGaussianMeanVarianceBayesianEstimator(UnivariateGaussian conditional,
NormalInverseGammaDistribution prior)
Creates a new instance of UnivariateGaussianMeanVarianceBayesianEstimator
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| Modifier and Type | Method and Description |
|---|---|
double |
computeEquivalentSampleSize(NormalInverseGammaDistribution belief)
Computes the equivalent sample size of using the given prior.
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UnivariateGaussianMeanVarianceBayesianEstimator.Parameter |
createParameter(UnivariateGaussian conditional,
NormalInverseGammaDistribution prior)
Creates a parameter linking the conditional and prior distributions
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StudentTDistribution |
createPredictiveDistribution(NormalInverseGammaDistribution posterior)
Creates the predictive distribution of new data given the posterior.
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void |
update(NormalInverseGammaDistribution target,
java.lang.Double data)
The
update method updates an object of ResultType using
the given new data of type DataType, using some form of
"learning" algorithm. |
void |
update(NormalInverseGammaDistribution prior,
java.lang.Iterable<? extends java.lang.Double> 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. |
clone, createConditionalDistribution, createInitialLearnedObject, getInitialBelief, getParameter, setParameterlearn, learnequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcreateConditionalDistribution, getParameterlearnclonecreateInitialLearnedObjectpublic UnivariateGaussianMeanVarianceBayesianEstimator()
public UnivariateGaussianMeanVarianceBayesianEstimator(NormalInverseGammaDistribution prior)
prior - Conjugate priorpublic UnivariateGaussianMeanVarianceBayesianEstimator(UnivariateGaussian conditional, NormalInverseGammaDistribution prior)
conditional - Conditional distributionprior - Conjugate priorprotected UnivariateGaussianMeanVarianceBayesianEstimator(BayesianParameter<Vector,UnivariateGaussian,NormalInverseGammaDistribution> parameter)
parameter - Parameter that describes the relationship between the conditional and
conjugate priorpublic UnivariateGaussianMeanVarianceBayesianEstimator.Parameter createParameter(UnivariateGaussian conditional, NormalInverseGammaDistribution prior)
ConjugatePriorBayesianEstimatorcreateParameter in interface ConjugatePriorBayesianEstimator<java.lang.Double,Vector,UnivariateGaussian,NormalInverseGammaDistribution>conditional - Distribution from which observations are generatedprior - Distribution that generates parameters for the conditionalpublic void update(NormalInverseGammaDistribution target, java.lang.Double 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<java.lang.Double,NormalInverseGammaDistribution>target - The object to update.data - The new data for the learning algorithm to use to update
the object.public void update(NormalInverseGammaDistribution prior, java.lang.Iterable<? extends java.lang.Double> 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<java.lang.Double,NormalInverseGammaDistribution>update in class AbstractBatchAndIncrementalLearner<java.lang.Double,NormalInverseGammaDistribution>prior - The object to update.data - The Iterable containing data for the learning algorithm to use to
update the object.public double computeEquivalentSampleSize(NormalInverseGammaDistribution belief)
ConjugatePriorBayesianEstimatorcomputeEquivalentSampleSize in interface ConjugatePriorBayesianEstimator<java.lang.Double,Vector,UnivariateGaussian,NormalInverseGammaDistribution>belief - Prior belief to measure.public StudentTDistribution createPredictiveDistribution(NormalInverseGammaDistribution posterior)
BayesianEstimatorPredictorcreatePredictiveDistribution in interface BayesianEstimatorPredictor<java.lang.Double,Vector,NormalInverseGammaDistribution>posterior - Posterior distribution from which to compute the predictive posterior.