public class BinomialBayesianEstimator extends AbstractConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,BinomialDistribution,BetaDistribution> implements ConjugatePriorBayesianEstimatorPredictor<java.lang.Number,java.lang.Double,BinomialDistribution,BetaDistribution>
| Modifier and Type | Class and Description |
|---|---|
static class |
BinomialBayesianEstimator.Parameter
Parameter of this relationship
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| Modifier and Type | Field and Description |
|---|---|
static int |
DEFAULT_N
Default n, 1.
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parameter| Modifier | Constructor and Description |
|---|---|
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BinomialBayesianEstimator()
Creates a new instance of BinomialBayesianEstimator
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protected |
BinomialBayesianEstimator(BayesianParameter<java.lang.Double,BinomialDistribution,BetaDistribution> parameter)
Creates a new instance of BinomialBayesianEstimator
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BinomialBayesianEstimator(BinomialDistribution conditional,
BetaDistribution prior)
Creates a new instance of BinomialBayesianEstimator
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BinomialBayesianEstimator(int n)
Creates a new instance of BinomialBayesianEstimator
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BinomialBayesianEstimator(int n,
BetaDistribution prior)
Creates a new instance of BinomialBayesianEstimator
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| Modifier and Type | Method and Description |
|---|---|
BinomialBayesianEstimator |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
double |
computeEquivalentSampleSize(BetaDistribution belief)
Computes the equivalent sample size of using the given prior.
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BinomialBayesianEstimator.Parameter |
createParameter(BinomialDistribution conditional,
BetaDistribution prior)
Creates a parameter linking the conditional and prior distributions
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BetaBinomialDistribution |
createPredictiveDistribution(BetaDistribution posterior)
Creates the predictive distribution of new data given the posterior.
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int |
getN()
Gets the number of samples in the experiment
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void |
setN(int n)
Sets the number of samples in the experiment
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void |
update(BetaDistribution target,
java.lang.Number data)
The
update method updates an object of ResultType using
the given new data of type DataType, using some form of
"learning" algorithm. |
createConditionalDistribution, createInitialLearnedObject, getInitialBelief, getParameter, setParameterlearn, learn, updateequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitcreateConditionalDistribution, getParameterlearncreateInitialLearnedObject, updatepublic static final int DEFAULT_N
public BinomialBayesianEstimator()
public BinomialBayesianEstimator(int n)
n - Samples in the experiment, must be greater than zeropublic BinomialBayesianEstimator(int n,
BetaDistribution prior)
n - Samples in the experiment, must be greater than zeroprior - Conjugate prior of the conditional for the parameterpublic BinomialBayesianEstimator(BinomialDistribution conditional, BetaDistribution prior)
conditional - Distribution that generates the observationsprior - Conjugate prior of the conditional for the parameterprotected BinomialBayesianEstimator(BayesianParameter<java.lang.Double,BinomialDistribution,BetaDistribution> parameter)
parameter - Parameter that describes the relationship between the conditional and
the conjugate priorpublic BinomialBayesianEstimator.Parameter createParameter(BinomialDistribution conditional, BetaDistribution prior)
ConjugatePriorBayesianEstimatorcreateParameter in interface ConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,BinomialDistribution,BetaDistribution>conditional - Distribution from which observations are generatedprior - Distribution that generates parameters for the conditionalpublic BinomialBayesianEstimator clone()
AbstractCloneableSerializableObject class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone() to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone() as the basis for your implementation.clone in interface CloneableSerializableclone in class AbstractConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,BinomialDistribution,BetaDistribution>public void update(BetaDistribution target, java.lang.Number 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.Number,BetaDistribution>target - The object to update.data - The new data for the learning algorithm to use to update
the object.public double computeEquivalentSampleSize(BetaDistribution belief)
ConjugatePriorBayesianEstimatorcomputeEquivalentSampleSize in interface ConjugatePriorBayesianEstimator<java.lang.Number,java.lang.Double,BinomialDistribution,BetaDistribution>belief - Prior belief to measure.public BetaBinomialDistribution createPredictiveDistribution(BetaDistribution posterior)
BayesianEstimatorPredictorcreatePredictiveDistribution in interface BayesianEstimatorPredictor<java.lang.Number,java.lang.Double,BetaDistribution>posterior - Posterior distribution from which to compute the predictive posterior.public int getN()
public void setN(int n)
n - Samples in the experiment, must be greater than zero