Comparer des méthodes

	Bayesian Latent Class Analysis	Analyse de classes latentes (ACL)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	1950s–1968
Auteur d'origine	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)	Paul F. Lazarsfeld
Type	Bayesian latent variable / finite mixture model	Latent variable / person-centered classification
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.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Alias	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.