Comparer des méthodes

	Bayesian Latent Class Analysis	Analyse de classification bayésienne
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	1998–2002
Auteur d'origine	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)
Type	Bayesian latent variable / finite mixture model	Probabilistic / model-based clustering
Source fondatrice	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.
Alias	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
Apparentées	6	6
Résumé	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.