Σύγκριση μεθόδων

	Κανονικοποιημένο Γκαουσιανό Μοντέλο Μίξης	Μοντέλο Μίγματος Γκαουσιανών Bayes
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2000s–2010s	1999–2006
Δημιουργός	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)	Attias, H.; Bishop, C. M.
Τύπος	Probabilistic clustering with regularization	Probabilistic clustering / density estimation
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
Συναφείς	5	4
Σύνοψη	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.