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Simulate Data from Gaussian Mixture Model

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This example shows how to simulate data from a Gaussian mixture model (GMM) using a fully specified gmdistribution object and the random function.

Create a known, two-component GMM object.

mu = [1 2;-3 -5];
sigma = cat(3,[2 0;0 .5],[1 0;0 1]);
p = ones(1,2)/2;
gm = gmdistribution(mu,sigma,p);

Plot the contour of the pdf of the GMM.

gmPDF = @(x,y) arrayfun(@(x0,y0) pdf(gm,[x0 y0]),x,y);
fcontour(gmPDF,[-10 10]);
title('Contour lines of pdf');

Figure contains an axes object. The axes object with title Contour lines of pdf contains an object of type functioncontour.

Generate 1000 random variates from the GMM. 

rng('default') % For reproducibility
X = random(gm,1000);

Plot the variates with the pdf contours.

hold on
scatter(X(:,1),X(:,2),10,'.') % Scatter plot with points of size 10
title('Contour lines of pdf and Simulated Data')

Figure contains an axes object. The axes object with title Contour lines of pdf and Simulated Data contains 2 objects of type functioncontour, scatter.

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