I am looking a C++ implementation of a multi-variate GMM that uses a Gibbs Sampling based approach to fitting / classification (rather than the usual EM based), in order to be able to make full use of a priori information and add in constraints. Often known as a Dirichlet Process Gaussian Mixture Model or DPGMM.
I already have this implemented in Matlab, but rather than spending time converting this code (yes I code use the built in matlab coder to convert, but it currently relies on various additional Matlab libraries). Also efficiency is important, I will be fitting a GMM to large data sets many times a second.
Thus, I am interested to know if there was already well known efficient code out there. An initial search hasn't returned very much.
