The cat function concatenates the covariances along the third array dimension. The defined covariance matrices are diagonal matrices. sigma(1,:,i) contains the diagonal elements of the covariance matrix of component i.

Create a gmdistribution object by using the gmdistribution function.

gm = gmdistribution(mu,sigma)

gm =
Gaussian mixture distribution with 2 components in 2 dimensions
Component 1:
Mixing proportion: 0.500000
Mean: 1 2
Component 2:
Mixing proportion: 0.500000
Mean: -3 -5

Plot the pdf of the Gaussian mixture distribution by using fsurf.

gm — Gaussian mixture distribution gmdistribution object

Gaussian mixture distribution, also called Gaussian mixture model (GMM), specified as a gmdistribution object.

You can create a gmdistribution object using gmdistribution or fitgmdist. Use the gmdistribution function to create a
gmdistribution object by specifying the distribution parameters.
Use the fitgmdist function to fit a gmdistribution
model to data given a fixed number of components.

X — Values at which to evaluate pdf n-by-m numeric matrix

Values at which to evaluate the pdf, specified as an
n-by-m numeric matrix, where
n is the number of observations and
m is the number of variables in each
observation.

where L(C_{j}|O_{j}) is the likelihood of component j given
observation i, and P(C_{j}) is the probability of component j. The
pdf function computes the likelihood term by using
the multivariate normal pdf of the jth Gaussian mixture
component evaluated at observation i. The component
probabilities are the mixing proportions of mixture components, the
ComponentProportion property of
gm.

You can also select a web site from the following list:

How to Get Best Site Performance

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.