Multivariate Distributions

Compute, fit, or generate samples from vector-valued distributions

A multivariate probability distribution is one that contains more than one random variable. These random variables might or might not be correlated. Statistics and Machine Learning Toolbox™ offers several ways to work with multivariate probability distributions, including probability distribution objects, command line functions, and interactive apps. For more information on these options, see Working with Probability Distributions.

Categories

Copula Distributions and Correlated Samples
Fit parameters of a model of correlated random samples to data, evaluate the distribution, generate serially correlated pseudorandom samples
Gaussian Mixture Distribution
Fit, evaluate, and generate random samples from Gaussian mixture distribution
Inverse Wishart Distribution
Generate pseudorandom samples from the inverse Wishart distribution
Multivariate Normal Distribution
Evaluate the multivariate normal (Gaussian) distribution, generate pseudorandom samples
Multivariate t Distribution
Evaluate the multivariate t distribution, generate pseudorandom samples
Wishart Distribution
Generate pseudorandom samples from the Wishart distribution