Порівняння методів

	Онлайнова гаусова суміш (Online Gaussian Mixture Model)	Байєсівська гауссова суміш
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	2000–2009	1999–2006
Автор методу	Cappé, O. & Moulines, E. (online EM formulation)	Attias, H.; Bishop, C. M.
Тип	Probabilistic clustering / density estimation (incremental)	Probabilistic clustering / density estimation
Основоположне джерело	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
Інші назви	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Пов'язані	5	4
Підсумок	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.