This package solves the Dirichlet Process Gaussian Mixture Model (aka Infinite GMM) with Gibbs sampling. This is nonparametric Bayesian treatment for mixture model problems which automatically selects the proper number of the clusters.
I includes the Gaussian component distribution in the package. However, the code is flexible enough for Dirichlet process mixture model of any distribution. User can write your own class for the base distribution then let the underlying Gibbs sampling engine do the inference work.
Please try the demo script in the package.
This package is now a part of the PRML toolbox (http://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox).
Mo Chen (2020). Dirichlet Process Gaussian Mixture Model (https://www.mathworks.com/matlabcentral/fileexchange/55865-dirichlet-process-gaussian-mixture-model), MATLAB Central File Exchange. Retrieved .
Very good work. It will be better if the author provides references.
Is there any detail description of the algorithm, just like you did for Variational Bayesian Inference for Gaussian Mixture Model?
Is it applicable for 1D data as well?
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