Gaussian Mixture Models

Cluster based on Gaussian mixture models using the Expectation-Maximization algorithm

Gaussian mixture models (GMMs) assign each observation to a cluster by maximizing the posterior probability that a data point belongs to its assigned cluster. Create a GMM 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 model (cdf, pdf), and generate random variates (random).

Functions

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Create `gmdistribution` Object

`fitgmdist`	Fit Gaussian mixture model to data
`gmdistribution`	Create Gaussian mixture model

Work with `gmdistribution` Object

`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