smile.stat.distribution

Interface MultivariateDistribution

All Known Implementing Classes:

AbstractMultivariateDistribution, MultivariateExponentialFamilyMixture, MultivariateGaussianDistribution, MultivariateGaussianMixture, MultivariateMixture

public interface MultivariateDistribution

Probability distribution of multivariate random variable.

Author:

Haifeng Li

See Also:

Distribution

Method Summary

Methods

Modifier and Type	Method and Description
double	cdf(double[] x) Cumulative distribution function.
double[][]	cov() The covariance matrix of distribution.
double	entropy() Shannon entropy of the distribution.
double	likelihood(double[][] x) The likelihood of the sample set following this distribution.
double	logLikelihood(double[][] x) The log likelihood of the sample set following this distribution.
double	logp(double[] x) The density at x in log scale, which may prevents the underflow problem.
double[]	mean() The mean vector of distribution.
int	npara() The number of parameters of the distribution.
double	p(double[] x) The probability density function for continuous distribution or probability mass function for discrete distribution at x.

Method Detail

npara

int npara()

The number of parameters of the distribution.

entropy

double entropy()

Shannon entropy of the distribution.

mean

double[] mean()

The mean vector of distribution.

cov

double[][] cov()

The covariance matrix of distribution.

p

double p(double[] x)

The probability density function for continuous distribution or probability mass function for discrete distribution at x.

logp

double logp(double[] x)

The density at x in log scale, which may prevents the underflow problem.

cdf

double cdf(double[] x)

Cumulative distribution function. That is the probability to the left of x.

likelihood

double likelihood(double[][] x)

The likelihood of the sample set following this distribution.

Parameters:

x - sample set. Each row is a sample.

logLikelihood

double logLikelihood(double[][] x)

The log likelihood of the sample set following this distribution.

Parameters:

x - sample set. Each row is a sample.