Comparer des méthodes

	Modèle de mélange gaussien régularisé	Modèle Bayésien de Mélange Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	1999–2006
Auteur d'origine	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)	Attias, H.; Bishop, C. M.
Type	Probabilistic clustering with regularization	Probabilistic clustering / density estimation
Source fondatrice	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2
Alias	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Apparentées	5	4
Résumé	A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations.	The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets.