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 (
mahal), evaluate the
|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|
- 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
gmdistributionobject and the
- Cluster Using Gaussian Mixture Model
Partition data into clusters with different sizes and correlation structures.