@PublicationReference(author={"Andrew Gelman","John B. Carlin","Hal S. Stern","Donald B. Rubin"}, title="Bayesian Data Analysis, Second Edition", type=Book, year=2004, pages=582, notes="Equation A.3") public static class BetaDistribution.MomentMatchingEstimator extends AbstractCloneableSerializable implements DistributionEstimator<java.lang.Double,BetaDistribution>
Constructor and Description |
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MomentMatchingEstimator()
Default constructor
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Modifier and Type | Method and Description |
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BetaDistribution |
learn(java.util.Collection<? extends java.lang.Double> data)
The
learn method creates an object of ResultType using
data of type DataType , using some form of "learning" algorithm. |
static BetaDistribution |
learn(double mean,
double variance)
Computes the Beta distribution describes by the given moments
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clone
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public BetaDistribution learn(java.util.Collection<? extends java.lang.Double> data)
BatchLearner
learn
method creates an object of ResultType
using
data of type DataType
, using some form of "learning" algorithm.learn
in interface BatchLearner<java.util.Collection<? extends java.lang.Double>,BetaDistribution>
data
- The data that the learning algorithm will use to create an
object of ResultType
.public static BetaDistribution learn(double mean, double variance)
mean
- Mean of the distributionvariance
- Variance of the distribution