public static class StudentTDistribution.MaximumLikelihoodEstimator extends AbstractCloneableSerializable implements DistributionEstimator<java.lang.Double,StudentTDistribution>
Modifier and Type | Field and Description |
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static double |
DEFAULT_VARIANCE
Typical value of a defaultVariance, 1.0E-5
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Constructor and Description |
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MaximumLikelihoodEstimator()
Default constructor
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MaximumLikelihoodEstimator(double defaultVariance)
Creates a new instance of MaximumLikelihoodEstimator
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Modifier and Type | Method and Description |
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StudentTDistribution |
learn(java.util.Collection<? extends java.lang.Double> data)
Creates a new instance of UnivariateGaussian from the given data
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static StudentTDistribution.PDF |
learn(java.util.Collection<? extends java.lang.Double> data,
double defaultVariance)
Creates a new instance of UnivariateGaussian from the given data
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clone
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public static final double DEFAULT_VARIANCE
public MaximumLikelihoodEstimator()
public MaximumLikelihoodEstimator(double defaultVariance)
defaultVariance
- Amount to add to the variance to keep it from being 0.0public StudentTDistribution learn(java.util.Collection<? extends java.lang.Double> data)
learn
in interface BatchLearner<java.util.Collection<? extends java.lang.Double>,StudentTDistribution>
data
- Data to fit a UnivariateGaussian againstpublic static StudentTDistribution.PDF learn(java.util.Collection<? extends java.lang.Double> data, double defaultVariance)
data
- Data to fit a UnivariateGaussian againstdefaultVariance
- Amount to add to the variance to keep it from being 0.0