gov.sandia.cognition.statistics.distribution

Class StudentTDistribution.WeightedMaximumLikelihoodEstimator

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.distribution.StudentTDistribution.WeightedMaximumLikelihoodEstimator

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends WeightedValue<? extends java.lang.Double>>,StudentTDistribution>, DistributionWeightedEstimator<java.lang.Double,StudentTDistribution>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

StudentTDistribution

public static class StudentTDistribution.WeightedMaximumLikelihoodEstimator
extends AbstractCloneableSerializable
implements DistributionWeightedEstimator<java.lang.Double,StudentTDistribution>

Creates a UnivariateGaussian from weighted data

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
WeightedMaximumLikelihoodEstimator() Default constructor
WeightedMaximumLikelihoodEstimator(double defaultVariance) Creates a new instance of WeightedMaximumLikelihoodEstimator

Method Summary

Modifier and Type	Method and Description
StudentTDistribution.PDF	learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data) Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
static StudentTDistribution.PDF	learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data, double defaultVariance) Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data

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

WeightedMaximumLikelihoodEstimator

public WeightedMaximumLikelihoodEstimator()

Default constructor

WeightedMaximumLikelihoodEstimator

public WeightedMaximumLikelihoodEstimator(double defaultVariance)

Creates a new instance of WeightedMaximumLikelihoodEstimator

Parameters:

defaultVariance - Amount to add to the variance to keep it from being 0.0

Method Detail

learn

public StudentTDistribution.PDF learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data)

Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data

Specified by:

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

Parameters:

data - Weighed pairs of data (first is data, second is weight) that was generated by some unknown UnivariateGaussian distribution

Returns:

Maximum Likelihood UnivariateGaussian that generated the data

learn

public static StudentTDistribution.PDF learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data,
                                             double defaultVariance)

Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data

Parameters:

data - Weighed pairs of data (first is data, second is weight) that was generated by some unknown UnivariateGaussian distribution

defaultVariance - Amount to add to the variance to keep it from being 0.0

Returns:

Maximum Likelihood UnivariateGaussian that generated the data