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?