Confronta i metodi

	Modello Gaussiano di Miscela Online	Modello Bayesiano a Mischia di Gaussiane
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2000–2009	1999–2006
Ideatore	Cappé, O. & Moulines, E. (online EM formulation)	Attias, H.; Bishop, C. M.
Tipo	Probabilistic clustering / density estimation (incremental)	Probabilistic clustering / density estimation
Fonte seminale	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
Correlati	5	4
Sintesi	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.