# Interface MultivariateDistribution

All Superinterfaces:
`Serializable`
All Known Implementing Classes:
`MultivariateExponentialFamilyMixture`, `MultivariateGaussianDistribution`, `MultivariateGaussianMixture`, `MultivariateMixture`

public interface MultivariateDistribution extends Serializable
Probability distribution of multivariate random variable.
• ## Method Summary

Modifier and Type
Method
Description
`double`
`cdf(double[] x)`
Cumulative distribution function.
`Matrix`
`cov()`
The covariance matrix of distribution.
`double`
`entropy()`
Shannon's entropy of the distribution.
`int`
`length()`
The number of parameters of the distribution.
`default double`
`likelihood(double[][] x)`
The likelihood of the sample set following this distribution.
`default 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.
`double`
`p(double[] x)`
The probability density function for continuous distribution or probability mass function for discrete distribution at x.
• ## Method Details

• ### length

int length()
The number of parameters of the distribution. The "length" is in the sense of the minimum description length principle.
Returns:
the number of parameters of the distribution.
• ### entropy

double entropy()
Shannon's entropy of the distribution.
Returns:
Shannon entropy
• ### mean

double[] mean()
The mean vector of distribution.
Returns:
the mean vector.
• ### cov

Matrix cov()
The covariance matrix of distribution.
Returns:
the covariance matrix.
• ### p

double p(double[] x)
The probability density function for continuous distribution or probability mass function for discrete distribution at x.
Parameters:
`x` - a real vector.
Returns:
the desnity.
• ### logp

double logp(double[] x)
The density at x in log scale, which may prevents the underflow problem.
Parameters:
`x` - a real vector.
Returns:
the log density.
• ### cdf

double cdf(double[] x)
Cumulative distribution function. That is the probability to the left of x.
Parameters:
`x` - a real vector.
Returns:
the probability.
• ### likelihood

default double likelihood(double[][] x)
The likelihood of the sample set following this distribution.
Parameters:
`x` - a set of samples.
Returns:
the likelihood.
• ### logLikelihood

default double logLikelihood(double[][] x)
The log likelihood of the sample set following this distribution.
Parameters:
`x` - a set of samples.
Returns:
the log likelihood.