Simulate Data from Gaussian Mixture Model
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');
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')
See Also
fitgmdist
| gmdistribution
| mvnrnd
| random