Comparer des méthodes

	Modèle gaussien de mélange en ligne	Modèle Bayésien de Mélange Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000–2009	1999–2006
Auteur d'origine	Cappé, O. & Moulines, E. (online EM formulation)	Attias, H.; Bishop, C. M.
Type	Probabilistic clustering / density estimation (incremental)	Probabilistic clustering / density estimation
Source fondatrice	Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2
Alias	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Apparentées	5	4
Résumé	Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.	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.