@PublicationReference(author="William M. Bolstad", title="Introduction to Bayesian Statistics: Second Edition", type=Book, year=2007, pages=185, notes={"Bolstad primarily uses INVERSE shape parameter on gamma!","So we must invert his calculations for shape!"}) public class PoissonBayesianEstimator extends AbstractConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,PoissonDistribution,GammaDistribution> implements ConjugatePriorBayesianEstimatorPredictor<java.lang.Number,java.lang.Double,PoissonDistribution,GammaDistribution>
| Modifier and Type | Class and Description |
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static class |
PoissonBayesianEstimator.Parameter
Parameter of this conjugate prior relationship.
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parameter| Modifier | Constructor and Description |
|---|---|
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PoissonBayesianEstimator()
Creates a new instance of PoissonBayesianEstimator
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protected |
PoissonBayesianEstimator(BayesianParameter<java.lang.Double,PoissonDistribution,GammaDistribution> parameter)
Creates a new instance
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PoissonBayesianEstimator(GammaDistribution belief)
Creates a new instance of PoissonBayesianEstimator
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PoissonBayesianEstimator(PoissonDistribution conditional,
GammaDistribution prior)
Creates a new instance of PoissonBayesianEstimator
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| Modifier and Type | Method and Description |
|---|---|
double |
computeEquivalentSampleSize(GammaDistribution belief)
Computes the equivalent sample size of using the given prior.
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PoissonBayesianEstimator.Parameter |
createParameter(PoissonDistribution conditional,
GammaDistribution prior)
Creates a parameter linking the conditional and prior distributions
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NegativeBinomialDistribution |
createPredictiveDistribution(GammaDistribution posterior)
Creates the predictive distribution of new data given the posterior.
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void |
update(GammaDistribution belief,
java.lang.Number value)
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, learn, updateequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcreateConditionalDistribution, getParameterlearnclonecreateInitialLearnedObject, updatepublic PoissonBayesianEstimator()
public PoissonBayesianEstimator(GammaDistribution belief)
belief - Conjugate prior belief.public PoissonBayesianEstimator(PoissonDistribution conditional, GammaDistribution prior)
prior - Default conjugate prior.conditional - Conditional distribution of the conjugate prior.protected PoissonBayesianEstimator(BayesianParameter<java.lang.Double,PoissonDistribution,GammaDistribution> parameter)
parameter - Bayesian hyperparameter relationship between the conditional
distribution and the conjugate prior distribution.public PoissonBayesianEstimator.Parameter createParameter(PoissonDistribution conditional, GammaDistribution prior)
ConjugatePriorBayesianEstimatorcreateParameter in interface ConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,PoissonDistribution,GammaDistribution>conditional - Distribution from which observations are generatedprior - Distribution that generates parameters for the conditionalpublic double computeEquivalentSampleSize(GammaDistribution belief)
ConjugatePriorBayesianEstimatorcomputeEquivalentSampleSize in interface ConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,PoissonDistribution,GammaDistribution>belief - Prior belief to measure.public void update(GammaDistribution belief, java.lang.Number value)
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.Number,GammaDistribution>belief - The object to update.value - The new data for the learning algorithm to use to update
the object.public NegativeBinomialDistribution createPredictiveDistribution(GammaDistribution posterior)
BayesianEstimatorPredictorcreatePredictiveDistribution in interface BayesianEstimatorPredictor<java.lang.Number,java.lang.Double,GammaDistribution>posterior - Posterior distribution from which to compute the predictive posterior.