org.openimaj.math.statistics.distribution

Interface MultivariateDistribution

All Known Subinterfaces:

MultivariateGaussian

All Known Implementing Classes:

AbstractMultivariateDistribution, AbstractMultivariateGaussian, CachingMultivariateGaussian, DiagonalMultivariateGaussian, FullMultivariateGaussian, GaussianMixtureModelEM.EMGMM, MixtureOfGaussians, MultivariateKernelDensityEstimate, SphericalMultivariateGaussian

public interface MultivariateDistribution

A continuous multivariate distribution.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Method Summary

Modifier and Type	Method and Description
double	estimateLogProbability(double[] sample) Get the log probability for a given point in space relative to the PDF represented by this distribution.
double[]	estimateLogProbability(double[][] x) Get the log probability for a given point in space relative to the PDF represented by this distribution.
double	estimateProbability(double[] sample) Get the probability for a given point in space relative to the PDF represented by this distribution.
double[][]	sample(int count, Random rng) Sample the distribution.
double[]	sample(Random rng) Sample the distribution.

Method Detail

sample

double[][] sample(int count,
                  Random rng)

Sample the distribution.

Parameters:

count - the number of samples to draw

rng - the random number generator

Returns:

a list of sample vectors from this distribution

sample

double[] sample(Random rng)

Sample the distribution. the number of samples to draw

Parameters:

rng - the random number generator

Returns:

a list of sample vectors from this distribution

estimateProbability

double estimateProbability(double[] sample)

Get the probability for a given point in space relative to the PDF represented by this distribution.

Parameters:

sample - the point

Returns:

the probability

estimateLogProbability

double estimateLogProbability(double[] sample)

Get the log probability for a given point in space relative to the PDF represented by this distribution.

Parameters:

sample - the point

Returns:

the log-probability

estimateLogProbability

double[] estimateLogProbability(double[][] x)

Get the log probability for a given point in space relative to the PDF represented by this distribution.

Parameters:

x - the samples

Returns:

the log-probability