পদ্ধতির তুলনা করুন

	রেগুলারাইজড গাউসিয়ান মিক্সচার মডেল	বেয়েশীয় গাউসীয় মিশ্রণ মডেল (Bayesian Gaussian Mixture Model)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000s–2010s	1999–2006
প্রবর্তক	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)	Attias, H.; Bishop, C. M.
ধরন	Probabilistic clustering with regularization	Probabilistic clustering / density estimation
মৌলিক উৎস	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
অপর নাম	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture
সম্পর্কিত	5	4
সারসংক্ষেপ	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.