gov.sandia.cognition.statistics.distribution

Class MultivariateGaussian.PDF

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

gov.sandia.cognition.statistics.distribution.MultivariateGaussian

gov.sandia.cognition.statistics.distribution.MultivariateGaussian.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>, EstimableDistribution<Vector,MultivariateGaussian>, ProbabilityDensityFunction<Vector>, ProbabilityFunction<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

MultivariateGaussian

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

PDF of a multivariate Gaussian

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

MultivariateGaussian.IncrementalEstimator, MultivariateGaussian.IncrementalEstimatorCovarianceInverse, MultivariateGaussian.MaximumLikelihoodEstimator, MultivariateGaussian.PDF, MultivariateGaussian.SufficientStatistic, MultivariateGaussian.SufficientStatisticCovarianceInverse, MultivariateGaussian.WeightedMaximumLikelihoodEstimator

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

DEFAULT_COVARIANCE_SYMMETRY_TOLERANCE, DEFAULT_DIMENSIONALITY, LOG_TWO_PI

Constructor Summary

Constructors

Constructor and Description
PDF() Default constructor.
PDF(int dimensionality) Creates a new instance of MultivariateGaussian.
PDF(MultivariateGaussian other) Creates a new instance of MultivariateGaussian.
PDF(Vector mean, Matrix covariance) Creates a new instance of MultivariateGaussian.

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.
MultivariateGaussian.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.

Methods inherited from class gov.sandia.cognition.statistics.distribution.MultivariateGaussian

clone, computeZSquared, convertFromVector, convertToVector, convolve, equals, getCovariance, getCovarianceInverse, getEstimator, getInputDimensionality, getLogCovarianceDeterminant, getLogLeadingCoefficient, getMean, hashCode, plus, sample, sample, sampleInto, sampleInto, scale, setCovariance, setCovariance, setCovarianceInverse, setCovarianceInverse, setMean, times, toString

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

sample, sample

Methods inherited from class java.lang.Object

finalize, getClass, notify, notifyAll, 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()

Default constructor.

PDF

public PDF(int dimensionality)

Creates a new instance of MultivariateGaussian.

Parameters:

dimensionality - Dimensionality of the Gaussian to create.

PDF

public PDF(Vector mean,
           Matrix covariance)

Creates a new instance of MultivariateGaussian.

Parameters:

mean - The mean of the Gaussian distribution.

covariance - The covariance matrix, which should be a symmetric matrix.

PDF

public PDF(MultivariateGaussian other)

Creates a new instance of MultivariateGaussian.

Parameters:

other - The other MultivariateGaussian to copy.

Method Detail

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.

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.

getProbabilityFunction

public MultivariateGaussian.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 MultivariateGaussian

Returns:

Distribution function associated with this Distribution.