পদ্ধতির তুলনা করুন

	বেয়েশীয় বৈষম্যমূলক বিশ্লেষণ (Bayesian Discriminant Analysis)	বেয়েশীয় গুচ্ছ বিশ্লেষণ
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1964	1998–2002
প্রবর্তক	Seymour Geisser	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
ধরন	Supervised classification / Bayesian inference	Probabilistic / model-based clustering
মৌলিক উৎস	Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
অপর নাম	BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
সম্পর্কিত	4	6
সারসংক্ষেপ	Bayesian discriminant analysis assigns observations to predefined groups by combining a multivariate Gaussian likelihood for each class with prior distributions over the class means and covariance matrices. Posterior predictive probabilities replace point-estimate decision boundaries, providing principled uncertainty quantification for classification in small or high-dimensional samples.	Bayesian cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.