# Regenerate Data from a Gaussian Mixture Model

Assume that I have an expectation maximization (EM) trained Gaussian Mixture Model (GMM). So for eg. with three sources i end up with the parameters

 ([u1, u2, u3], [sigma1, sigma2, sigma3], [tau1, tau2, tau3]).


Is there a way to use this information plus a count of how many data points "d" that were used to make this model to regenerate data that would likely be similar to the original model?

Is it just feeding this Gaussian with random numbers? or is there a better way to do it?

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What's wrong with picking one of the Gaussians w/prob proportional to their weight and sampling from it (all repeated $d$ times)? Why do you need a better way? –  Yoav Kallus May 25 '12 at 3:08
I may not, I'm not really sure how to do this which is why I asked, you should add that as an answer. –  user23936 May 25 '12 at 5:02