gov.sandia.cognition.statistics.distribution

Class MultivariateStudentTDistribution.PDF

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<Vector>

gov.sandia.cognition.statistics.distribution.MultivariateStudentTDistribution

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

All Implemented Interfaces:

Evaluator<Vector,java.lang.Double>, VectorInputEvaluator<Vector,java.lang.Double>, Vectorizable, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, ProbabilityDensityFunction<Vector>, ProbabilityFunction<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

MultivariateStudentTDistribution

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

Constructors

Constructor and Description
PDF() Creates a new instance of MultivariateStudentTDistribution
PDF(double degreesOfFreedom, Vector mean, Matrix precision) Creates a distribution with the given dimensionality.
PDF(int dimensionality) Creates a distribution with the given dimensionality.
PDF(MultivariateStudentTDistribution other) Copy constructor

Method Summary

Modifier and Type	Method and Description
java.lang.Double	evaluate(Vector input) Evaluates the function on the given input and returns the output.
java.lang.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, sampleInto, setDegreesOfFreedom, setMean

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample, 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, sampleInto

Methods inherited from interface gov.sandia.cognition.math.matrix.VectorInputEvaluator

getInputDimensionality

Constructor Detail

PDF

public PDF()

Creates a new instance of MultivariateStudentTDistribution

PDF

public PDF(int dimensionality)

Creates a distribution with the given dimensionality.

Parameters:

dimensionality - Dimensionality of the distribution.

PDF

public 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.

PDF

public 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>

Parameters:

input - The input to be evaluated

Returns:

Natural logarithm of the distribution function.

evaluate

public java.lang.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,java.lang.Double>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

getLogDeterminantPrecision

public java.lang.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.