Compara mètodes

	Anàlisi Bayesiana de Classes Latents (BLCA)	Anàlisi bayesiana de clústers
Camp	Estadística	Estadística
Família	Latent structure	Latent structure
Any d'origen	1990s–2000s	1998–2002
Autor original	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)
Tipus	Bayesian latent variable / finite mixture model	Probabilistic / model-based clustering
Font seminal	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.
Àlies	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
Relacionats	6	6
Resum	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.