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 (
mahal), evaluate the
To learn about the Gaussian mixture distribution, see Gaussian Mixture Models.
|Cumulative distribution function for Gaussian mixture distribution|
|Construct clusters from Gaussian mixture distribution|
|Mahalanobis distance to Gaussian mixture component|
|Probability density function for Gaussian mixture distribution|
|Posterior probability of Gaussian mixture component|
|Random variate from Gaussian mixture distribution|
Gaussian mixture models (GMMs) contain k multivariate normal density components, where k is a positive integer.
Create a known, or fully specified, Gaussian mixture model (GMM) object.
Simulate data from a multivariate normal distribution, and then fit a Gaussian mixture model (GMM) to the data.
Simulate data from a Gaussian mixture model (GMM) using a fully specified
gmdistribution object and the
Partition data into clusters with different sizes and correlation structures.