## Interface MultivariateProbabilityDistribution

• All Known Subinterfaces:
BivariateEVD, BivariateProbabilityDistribution
All Known Implementing Classes:
AbstractBivariateEVD, AbstractBivariateProbabilityDistribution, BivariateEVDAsymmetricLogistic, BivariateEVDAsymmetricMixed, BivariateEVDAsymmetricNegativeLogistic, BivariateEVDBilogistic, BivariateEVDColesTawn, BivariateEVDHuslerReiss, BivariateEVDLogistic, BivariateEVDNegativeBilogistic, BivariateEVDNegativeLogistic, DirichletDistribution, MultinomialDistribution, MultivariateNormalDistribution, MultivariateTDistribution

public interface MultivariateProbabilityDistribution
A multivariate or joint probability distribution for X, Y, ... is a probability distribution that gives the probability that each of X, Y, ... falls in any particular range or discrete set of values specified for that variable.
Wikipedia: Joint probability distribution
• ### Method Summary

All Methods
Modifier and Type Method Description
double cdf​(Vector x)
Gets the cumulative probability F(x) = Pr(X ≤ x).
Matrix covariance()
Gets the covariance matrix of this distribution.
double density​(Vector x)
The density function, which, if exists, is the derivative of F.
double entropy()
Gets the entropy of this distribution.
Vector mean()
Gets the mean of this distribution.
Vector mode()
Gets the mode of this distribution.
double moment​(Vector t)
The moment generating function is the expected value of etX.
• ### Method Detail

• #### cdf

double cdf​(Vector x)
Gets the cumulative probability F(x) = Pr(X ≤ x).
Parameters:
x - x
Returns:
F(x) = Pr(X ≤ x)
• #### density

double density​(Vector x)
The density function, which, if exists, is the derivative of F. It describes the density of probability at each point in the sample space.
f(x) = dF(X) / dx
This may not always exist.

For the discrete cases, this is the probability mass function. It gives the probability that a discrete random variable is exactly equal to some value.

Parameters:
x - x
Returns:
f(x)
• #### mean

Vector mean()
Gets the mean of this distribution.
Returns:
the mean
• #### mode

Vector mode()
Gets the mode of this distribution.
Returns:
the mean
• #### covariance

Matrix covariance()
Gets the covariance matrix of this distribution.
Returns:
the covariance
• #### moment

double moment​(Vector t)
The moment generating function is the expected value of etX. That is,
E(etX)
This may not always exist.
Parameters:
t - t
Returns:
E(exp(tX))