পদ্ধতির তুলনা করুন

	বেয়েশীয় গুচ্ছ বিশ্লেষণ	বেয়েশীয় ল্যাটেন্ট ক্লাস অ্যানালাইসিস (Bayesian Latent Class Analysis - BLCA)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	1998–2002	1990s–2000s
প্রবর্তক	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
ধরন	Probabilistic / model-based clustering	Bayesian latent variable / finite mixture model
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.	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.
অপর নাম	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
সম্পর্কিত	6	6
সারসংক্ষেপ	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.	Bayesian latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way.