smile.stat.distribution

## Interface MultivariateDistribution

• ### Method Summary

All Methods
Modifier and Type Method and Description
`double` `cdf(double[] x)`
Cumulative distribution function.
`Matrix` `cov()`
The covariance matrix of distribution.
`double` `entropy()`
Shannon 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 Detail

• #### length

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

`double entropy()`
Shannon entropy of the distribution.
• #### mean

`double[] mean()`
The mean vector of distribution.
• #### cov

`Matrix 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

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

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