How to use MAP estimate instead of Maximum Likelihood Estimate while modelling Gaussian Mixture Model for a data set? Please read description.

I want to fit GMMs to n data sets X(1), X(2), ....X(n) one-by-one. Each X(i); i ∈ (1, n), has 100 points which are plotted to be modelled by GMMs. I know that there are only two Gaussian components in each mixture. I am using fitgmdist to fit GMM to each X(i) separately. When fitgmdist reaches convergence for X(i), it returns mixing ratios of the two Gaussian components. I want to use this mixing ratio as the initial mixing ratio for the next data i.e. X(i+1). Since this is like using prior knowledge for fitting a GMM to X(i+1), MAP should be used instead of MLE. fitgmdist uses MLE by default. Is there a way to switch to MAP in the fitgmdist algorithm? Or use previous mixing ratios obtained for X(i) as initial parameter for X(i+1) while sticking with MLE? Kindly answer in detail if possible. I am new to MATLAB.

Answer 1

[Shruti Shivaramakrishnan]

Unfortunately, MATLAB currently does not have a built-in function for the MAP estimate calculation while modelling Gaussian Mixture Model for a data set. I work for MathWorks and have forwarded this feedback to the appropriate product team.