@PublicationReference(author="William M. Bolstad", title="Introduction to Bayesian Statistics: Second Edition", type=Book, year=2007, pages=208) public class UnivariateGaussianMeanBayesianEstimator extends AbstractConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian> implements ConjugatePriorBayesianEstimatorPredictor<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>
Modifier and Type | Class and Description |
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static class |
UnivariateGaussianMeanBayesianEstimator.Parameter
Parameter of this conjugate prior relationship.
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Modifier and Type | Field and Description |
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static double |
DEFAULT_KNOWN_VARIANCE
Default known variance of the estimated distribution, 1.0.
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parameter
Modifier | Constructor and Description |
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UnivariateGaussianMeanBayesianEstimator()
Creates a new instance of UnivariateGaussianMeanBayesianEstimator
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protected |
UnivariateGaussianMeanBayesianEstimator(BayesianParameter<java.lang.Double,UnivariateGaussian,UnivariateGaussian> parameter)
Creates a new instance
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UnivariateGaussianMeanBayesianEstimator(double knownVariance)
Creates a new instance of UnivariateGaussianMeanBayesianEstimator
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UnivariateGaussianMeanBayesianEstimator(double knownVariance,
UnivariateGaussian belief)
Creates a new instance of UnivariateGaussianMeanBayesianEstimator
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UnivariateGaussianMeanBayesianEstimator(UnivariateGaussian conditional,
UnivariateGaussian prior)
Creates a new instance
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Modifier and Type | Method and Description |
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double |
computeEquivalentSampleSize(UnivariateGaussian belief)
Computes the equivalent sample size of using the given prior.
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UnivariateGaussianMeanBayesianEstimator.Parameter |
createParameter(UnivariateGaussian conditional,
UnivariateGaussian prior)
Creates a parameter linking the conditional and prior distributions
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UnivariateGaussian |
createPredictiveDistribution(UnivariateGaussian posterior)
Creates the predictive distribution from the given posterior.
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double |
getKnownVariance()
Getter for knownVariance.
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void |
setKnownVariance(double knownVariance)
Setter for knownVariance.
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void |
update(UnivariateGaussian updater,
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. |
clone, createConditionalDistribution, createInitialLearnedObject, getInitialBelief, getParameter, setParameter
learn, learn, update
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
createConditionalDistribution, getParameter
learn
clone
createInitialLearnedObject, update
public static final double DEFAULT_KNOWN_VARIANCE
public UnivariateGaussianMeanBayesianEstimator()
public UnivariateGaussianMeanBayesianEstimator(double knownVariance)
knownVariance
- Known variance of the distribution.public UnivariateGaussianMeanBayesianEstimator(double knownVariance, UnivariateGaussian belief)
belief
- Conjugate prior of the posterior belief.knownVariance
- Known variance of the distribution.public UnivariateGaussianMeanBayesianEstimator(UnivariateGaussian conditional, UnivariateGaussian prior)
conditional
- Distribution from which observations are generatedprior
- Conjugate prior to the conditional distributionprotected UnivariateGaussianMeanBayesianEstimator(BayesianParameter<java.lang.Double,UnivariateGaussian,UnivariateGaussian> parameter)
parameter
- Bayesian hyperparameter relationship between the conditional
distribution and the conjugate prior distribution.public UnivariateGaussianMeanBayesianEstimator.Parameter createParameter(UnivariateGaussian conditional, UnivariateGaussian prior)
ConjugatePriorBayesianEstimator
createParameter
in interface ConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>
conditional
- Distribution from which observations are generatedprior
- Distribution that generates parameters for the conditionalpublic double getKnownVariance()
public void setKnownVariance(double knownVariance)
knownVariance
- Known variance of the distribution.public void update(UnivariateGaussian updater, java.lang.Double 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<java.lang.Double,UnivariateGaussian>
updater
- The object to update.data
- The new data for the learning algorithm to use to update
the object.public double computeEquivalentSampleSize(UnivariateGaussian belief)
ConjugatePriorBayesianEstimator
computeEquivalentSampleSize
in interface ConjugatePriorBayesianEstimator<java.lang.Double,java.lang.Double,UnivariateGaussian,UnivariateGaussian>
belief
- Prior belief to measure.public UnivariateGaussian createPredictiveDistribution(UnivariateGaussian posterior)
createPredictiveDistribution
in interface BayesianEstimatorPredictor<java.lang.Double,java.lang.Double,UnivariateGaussian>
posterior
- Posterior from which to create the predictive distribution