방법 비교

	베이지안 잠재계층 분석 (Bayesian Latent Class Analysis, BLCA)	베이지안 군집 분석
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	1990s–2000s	1998–2002
창시자	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
유형	Bayesian latent variable / finite mixture model	Probabilistic / model-based clustering
원전	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
별칭	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
관련	6	6
요약	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.	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.