gov.sandia.cognition.statistics.distribution
Class GammaDistribution.WeightedMomentMatchingEstimator

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.distribution.GammaDistribution.WeightedMomentMatchingEstimator
All Implemented Interfaces:
BatchLearner<Collection<? extends WeightedValue<? extends Double>>,GammaDistribution>, DistributionWeightedEstimator<Double,GammaDistribution>, CloneableSerializable, Serializable, Cloneable
Enclosing class:
GammaDistribution

public static class GammaDistribution.WeightedMomentMatchingEstimator
extends AbstractCloneableSerializable
implements DistributionWeightedEstimator<Double,GammaDistribution>

Estimates the parameters of a Gamma distribution using the matching of moments, not maximum likelihood.

See Also:
Serialized Form

Constructor Summary
GammaDistribution.WeightedMomentMatchingEstimator()
          Default constructor
 
Method Summary
 GammaDistribution learn(Collection<? extends WeightedValue<? extends Double>> data)
          The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
 
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

GammaDistribution.WeightedMomentMatchingEstimator

public GammaDistribution.WeightedMomentMatchingEstimator()
Default constructor

Method Detail

learn

public GammaDistribution learn(Collection<? extends WeightedValue<? extends 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<Collection<? extends WeightedValue<? extends 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.