gov.sandia.cognition.statistics.distribution
Class MultivariateStudentTDistribution.PDF

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.AbstractDistribution<Vector>
          extended by gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
              extended by gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution.PDF
All Implemented Interfaces:
Evaluator<Vector,Double>, VectorInputEvaluator<Vector,Double>, Vectorizable, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, ProbabilityDensityFunction<Vector>, ProbabilityFunction<Vector>, CloneableSerializable, Serializable, Cloneable
Enclosing class:
MultivariateStudentTDistribution

public static class MultivariateStudentTDistribution.PDF
extends MultivariateStudentTDistribution
implements ProbabilityDensityFunction<Vector>, VectorInputEvaluator<Vector,Double>

PDF of the MultivariateStudentTDistribution

See Also:
Serialized Form

Nested Class Summary
 
Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
MultivariateStudentTDistribution.PDF
 
Field Summary
 
Fields inherited from class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
DEFAULT_DEGREES_OF_FREEDOM, DEFAULT_DIMENSIONALITY, degreesOfFreedom, mean
 
Constructor Summary
MultivariateStudentTDistribution.PDF()
          Creates a new instance of MultivariateStudentTDistribution
MultivariateStudentTDistribution.PDF(double degreesOfFreedom, Vector mean, Matrix precision)
          Creates a distribution with the given dimensionality.
MultivariateStudentTDistribution.PDF(int dimensionality)
          Creates a distribution with the given dimensionality.
MultivariateStudentTDistribution.PDF(MultivariateStudentTDistribution other)
          Copy constructor
 
Method Summary
 Double evaluate(Vector input)
          Evaluates the function on the given input and returns the output.
 Double getLogDeterminantPrecision()
          Getter for logDeterminantPrecision
 MultivariateStudentTDistribution.PDF getProbabilityFunction()
          Gets the distribution function associated with this Distribution, either the PDF or PMF.
 double logEvaluate(Vector input)
          Evaluate the natural logarithm of the distribution function.
 void setPrecision(Matrix precision)
          Setter for precision
 
Methods inherited from class gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution
clone, convertFromVector, convertToVector, getCovariance, getDegreesOfFreedom, getInputDimensionality, getMean, getPrecision, sample, setDegreesOfFreedom, setMean
 
Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution
sample
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.statistics.DistributionWithMean
getMean
 
Methods inherited from interface gov.sandia.cognition.math.matrix.Vectorizable
clone, convertFromVector, convertToVector
 
Methods inherited from interface gov.sandia.cognition.statistics.Distribution
sample, sample
 
Methods inherited from interface gov.sandia.cognition.math.matrix.VectorInputEvaluator
getInputDimensionality
 

Constructor Detail

MultivariateStudentTDistribution.PDF

public MultivariateStudentTDistribution.PDF()
Creates a new instance of MultivariateStudentTDistribution


MultivariateStudentTDistribution.PDF

public MultivariateStudentTDistribution.PDF(int dimensionality)
Creates a distribution with the given dimensionality.

Parameters:
dimensionality - Dimensionality of the distribution.

MultivariateStudentTDistribution.PDF

public MultivariateStudentTDistribution.PDF(double degreesOfFreedom,
                                            Vector mean,
                                            Matrix precision)
Creates a distribution with the given dimensionality.

Parameters:
degreesOfFreedom - Degrees of freedom in the distribution, usually the number of datapoints - 1, DOFs must be greater than zero.
mean - Mean, or noncentrality parameter, of the distribution
precision - Precision, which is proportionate to the inverse of variance, of the distribution, must be symmetric and positive definite.

MultivariateStudentTDistribution.PDF

public MultivariateStudentTDistribution.PDF(MultivariateStudentTDistribution other)
Copy constructor

Parameters:
other - MultivariateStudentTDistribution to copy
Method Detail

getProbabilityFunction

public MultivariateStudentTDistribution.PDF getProbabilityFunction()
Description copied from interface: ComputableDistribution
Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:
getProbabilityFunction in interface ComputableDistribution<Vector>
Specified by:
getProbabilityFunction in interface ProbabilityDensityFunction<Vector>
Overrides:
getProbabilityFunction in class MultivariateStudentTDistribution
Returns:
Distribution function associated with this Distribution.

logEvaluate

public double logEvaluate(Vector input)
Description copied from interface: ProbabilityFunction
Evaluate the natural logarithm of the distribution function. This is sometimes more efficient than evaluating the distribution function itself, and when evaluating the product of many independent or exchangeable samples.

Specified by:
logEvaluate in interface ProbabilityFunction<Vector>
Returns:
Natural logarithm of the distribution function.

evaluate

public Double evaluate(Vector input)
Description copied from interface: Evaluator
Evaluates the function on the given input and returns the output.

Specified by:
evaluate in interface Evaluator<Vector,Double>
Parameters:
input - The input to evaluate.
Returns:
The output produced by evaluating the input.

getLogDeterminantPrecision

public Double getLogDeterminantPrecision()
Getter for logDeterminantPrecision

Returns:
Log determinant of the precision matrix.

setPrecision

public void setPrecision(Matrix precision)
Description copied from class: MultivariateStudentTDistribution
Setter for precision

Overrides:
setPrecision in class MultivariateStudentTDistribution
Parameters:
precision - Precision, which is proportionate to the inverse of variance, of the distribution, must be symmetric and positive definite.