# 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`).

## Functions

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 `fitgmdist` Fit Gaussian mixture model to data `gmdistribution` Create Gaussian mixture model
 `cdf` Cumulative distribution function for Gaussian mixture distribution `cluster` Construct clusters from Gaussian mixture distribution `mahal` Mahalanobis distance to Gaussian mixture component `pdf` Probability density function for Gaussian mixture distribution `posterior` Posterior probability of Gaussian mixture component `random` Random variate from Gaussian mixture distribution

## Topics

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 Model

Partition data into clusters with different sizes and correlation structures.