So sánh phương pháp

	Mô hình hỗn hợp Gauss trực tuyến	Mô hình Hỗn hợp Gaussian Bayes
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000–2009	1999–2006
Người khởi xướng	Cappé, O. & Moulines, E. (online EM formulation)	Attias, H.; Bishop, C. M.
Loại	Probabilistic clustering / density estimation (incremental)	Probabilistic clustering / density estimation
Công trình gốc	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
Tên gọi khác	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Liên quan	5	4
Tóm tắt	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.