Gaussian Mixture Distribution

Fit, evaluate, and generate random samples from Gaussian mixture distribution

A Gaussian mixture distribution is a multivariate distribution that consists of multivariate Gaussian distribution components. Each component is defined by its mean and covariance, and the mixture is defined by a vector of mixing proportions. Create a distribution object gmdistribution by fitting a model to data (fitgmdist) or by specifying parameter values (gmdistribution). Then, use object functions to perform cluster analysis (cluster, posterior, mahal), evaluate the distribution (cdf, pdf), and generate random variates (random).

To learn about the Gaussian mixture distribution, see Gaussian Mixture Models.

Functions

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fitgmdistFit Gaussian mixture model to data
gmdistributionCreate Gaussian mixture model
cdfCumulative distribution function for Gaussian mixture distribution
clusterConstruct clusters from Gaussian mixture distribution
mahalMahalanobis distance to Gaussian mixture component
pdfProbability density function for Gaussian mixture distribution
posteriorPosterior probability of Gaussian mixture component
randomRandom variate from Gaussian mixture distribution

Topics

Gaussian Mixture Models

Gaussian mixture models (GMMs) contain k multivariate normal density components, where k is a positive integer.

Create Gaussian Mixture Model

Create a known, or fully specified, Gaussian mixture model (GMM) object.

Fit Gaussian Mixture Model to Data

Simulate data from a multivariate normal distribution, and then fit a Gaussian mixture model (GMM) to the data.

Simulate Data from Gaussian Mixture Model

Simulate data from a Gaussian mixture model (GMM) using a fully specified gmdistribution object and the random function.

Cluster Using Gaussian Mixture Models

Partition data into clusters with different sizes and correlation structures.