gov.sandia.cognition.statistics.distribution
Class MultivariateGaussian.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.MultivariateGaussian
              extended by gov.sandia.cognition.statistics.distribution.MultivariateGaussian.PDF
All Implemented Interfaces:
Evaluator<Vector,Double>, VectorInputEvaluator<Vector,Double>, Vectorizable, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, EstimableDistribution<Vector,MultivariateGaussian>, ProbabilityDensityFunction<Vector>, ProbabilityFunction<Vector>, CloneableSerializable, Serializable, Cloneable
Enclosing class:
MultivariateGaussian

public static class MultivariateGaussian.PDF
extends MultivariateGaussian
implements ProbabilityDensityFunction<Vector>, VectorInputEvaluator<Vector,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
MultivariateGaussian.PDF()
          Default constructor.
MultivariateGaussian.PDF(int dimensionality)
          Creates a new instance of MultivariateGaussian.
MultivariateGaussian.PDF(MultivariateGaussian other)
          Creates a new instance of MultivariateGaussian.
MultivariateGaussian.PDF(Vector mean, Matrix covariance)
          Creates a new instance of MultivariateGaussian.
 
Method Summary
 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, sample, scale, setCovariance, setCovariance, setCovarianceInverse, setCovarianceInverse, setMean, times, toString
 
Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution
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
 
Methods inherited from interface gov.sandia.cognition.math.matrix.VectorInputEvaluator
getInputDimensionality
 

Constructor Detail

MultivariateGaussian.PDF

public MultivariateGaussian.PDF()
Default constructor.


MultivariateGaussian.PDF

public MultivariateGaussian.PDF(int dimensionality)
Creates a new instance of MultivariateGaussian.

Parameters:
dimensionality - Dimensionality of the Gaussian to create.

MultivariateGaussian.PDF

public MultivariateGaussian.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.

MultivariateGaussian.PDF

public MultivariateGaussian.PDF(MultivariateGaussian other)
Creates a new instance of MultivariateGaussian.

Parameters:
other - The other MultivariateGaussian to copy.
Method Detail

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.

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.

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.