public static class StudentTDistribution.WeightedMaximumLikelihoodEstimator extends AbstractCloneableSerializable implements DistributionWeightedEstimator<java.lang.Double,StudentTDistribution>
Constructor and Description |
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WeightedMaximumLikelihoodEstimator()
Default constructor
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WeightedMaximumLikelihoodEstimator(double defaultVariance)
Creates a new instance of WeightedMaximumLikelihoodEstimator
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Modifier and Type | Method and Description |
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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
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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
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clone
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public WeightedMaximumLikelihoodEstimator()
public WeightedMaximumLikelihoodEstimator(double defaultVariance)
defaultVariance
- Amount to add to the variance to keep it from being 0.0public StudentTDistribution.PDF learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data)
learn
in interface BatchLearner<java.util.Collection<? extends WeightedValue<? extends java.lang.Double>>,StudentTDistribution>
data
- Weighed pairs of data (first is data, second is weight) that was
generated by some unknown UnivariateGaussian distributionpublic static StudentTDistribution.PDF learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data, double defaultVariance)
data
- Weighed pairs of data (first is data, second is weight) that was
generated by some unknown UnivariateGaussian distributiondefaultVariance
- Amount to add to the variance to keep it from being 0.0