手法を比較

	ベイズクラスター分析	ベイズ潜在クラス分析（BLCA）
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1998–2002	1990s–2000s
提唱者	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)
種類	Probabilistic / model-based clustering	Bayesian latent variable / finite mixture model
原典	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.
別名	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
関連	6	6
概要	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.