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, seeWorking 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
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