Comparar métodos

	Análisis de conglomerados bayesiano	Análisis Bayesiano de Clases Latentes (BLCA)
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	1998–2002	1990s–2000s
Autor original	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)
Tipo	Probabilistic / model-based clustering	Bayesian latent variable / finite mixture model
Fuente seminal	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.
Alias	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Relacionados	6	6
Resumen	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.