Comparer des méthodes

	Bayesian Latent Class Analysis	Analyse factorielle confirmatoire bayésienne (AFCB)
Domaine	Statistique	Psychométrie
Famille	Latent structure	Latent structure
Année d'origine	1990s–2000s	2007–2012
Auteur d'origine	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov
Type	Bayesian latent variable / finite mixture model	Bayesian latent variable model
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.	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232
Alias	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA
Apparentées	6	4
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 confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally.