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

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

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

Creates a UnivariateGaussian from weighted data

See Also:
Serialized Form

Constructor Summary
StudentTDistribution.WeightedMaximumLikelihoodEstimator()
          Default constructor
StudentTDistribution.WeightedMaximumLikelihoodEstimator(double defaultVariance)
          Creates a new instance of WeightedMaximumLikelihoodEstimator
 
Method Summary
 StudentTDistribution.PDF learn(Collection<? extends WeightedValue<? extends Double>> data)
          Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
static StudentTDistribution.PDF learn(Collection<? extends WeightedValue<? extends 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

StudentTDistribution.WeightedMaximumLikelihoodEstimator

public StudentTDistribution.WeightedMaximumLikelihoodEstimator()
Default constructor


StudentTDistribution.WeightedMaximumLikelihoodEstimator

public StudentTDistribution.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(Collection<? extends WeightedValue<? extends 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<Collection<? extends WeightedValue<? extends 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(Collection<? extends WeightedValue<? extends 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