Optimal Initial Value for Gaussian Mixture Model
Select the Best Gaussian Mixture Model (GMM) Based on BIC
Select the Best Initialization Method for a Gaussian Mixture Model (GM...
Initialize Parameters for the EM Algorithm in Gaussian Mixture Models
Optimal Initial Value for Gaussian Mixture Model
Expectation-Maximization (EM) Algorithm for Gaussian Mixture Models
Run Gaussian Mixture Model (GMM) Clustering with Multiple Initializati...
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) <doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003) <doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of Dempster et al. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on model-based clustering includes Fraley and Raftery (2002) <doi:10.1198/016214502760047131> and McLachlan and Peel (2000, ISBN:9780471006268).