Optimal Initial Value for Gaussian Mixture Model

Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) and Biernacki et al. (2003) , and on the EM algorithm of Dempster et al. (1977) . Background on model-based clustering includes Fraley and Raftery (2002) and McLachlan and Peel (2000, ISBN:9780471006268).