gov.sandia.cognition.statistics.distribution

Class GammaDistribution.MomentMatchingEstimator

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.distribution.GammaDistribution.MomentMatchingEstimator

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends java.lang.Double>,GammaDistribution>, DistributionEstimator<java.lang.Double,GammaDistribution>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

GammaDistribution

public static class GammaDistribution.MomentMatchingEstimator
extends AbstractCloneableSerializable
implements DistributionEstimator<java.lang.Double,GammaDistribution>

Computes the parameters of a Gamma distribution by the Method of Moments

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MomentMatchingEstimator() Default constructor

Method Summary

Modifier and Type	Method and Description
GammaDistribution	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 GammaDistribution	learn(double mean, double variance) Computes the Gamma distribution describes by the given moments

Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

clone

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Constructor Detail

MomentMatchingEstimator

public MomentMatchingEstimator()

Default constructor

Method Detail

learn

public GammaDistribution learn(java.util.Collection<? extends java.lang.Double> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends java.lang.Double>,GammaDistribution>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

learn

public static GammaDistribution learn(double mean,
                                      double variance)

Computes the Gamma distribution describes by the given moments

Parameters:

mean - Mean of the distribution

variance - Variance of the distribution

Returns:

Gamma distribution that has the same mean/variance as the given parameters.