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

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

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

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

See Also:
Serialized Form

Constructor Summary
GammaDistribution.MomentMatchingEstimator()
          Default constructor
 
Method Summary
 GammaDistribution learn(Collection<? extends 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

GammaDistribution.MomentMatchingEstimator

public GammaDistribution.MomentMatchingEstimator()
Default constructor

Method Detail

learn

public GammaDistribution learn(Collection<? 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 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.