@PublicationReference(author={"Ram C. Tripathi","Ramesh C. Gupta","John Gurland"}, title="Estimation of parameters in the beta binomial model", type=Journal, publication="Annals of the Institute of Statistical Mathematics", year=1994, pages={317,331}, notes="Equation 2.11") public static class BetaBinomialDistribution.MomentMatchingEstimator extends AbstractCloneableSerializable implements DistributionEstimator<java.lang.Number,BetaBinomialDistribution>
Constructor and Description |
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MomentMatchingEstimator()
Default constructor
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Modifier and Type | Method and Description |
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BetaBinomialDistribution |
learn(java.util.Collection<? extends java.lang.Number> data)
The
learn method creates an object of ResultType using
data of type DataType , using some form of "learning" algorithm. |
static BetaBinomialDistribution.PMF |
learn(int N,
double mean,
double variance)
Computes the Beta-Binomial distribution describes by the given moments
|
clone
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public BetaBinomialDistribution learn(java.util.Collection<? extends java.lang.Number> 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.Number>,BetaBinomialDistribution>
data
- The data that the learning algorithm will use to create an
object of ResultType
.public static BetaBinomialDistribution.PMF learn(int N, double mean, double variance)
N
- Number of trialsmean
- Mean of the distributionvariance
- Variance of the distribution