Сравнение методов

	Байесовский кластерный анализ	Байесовский анализ латентных классов (BLCA)
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	1998–2002	1990s–2000s
Автор метода	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)
Тип	Probabilistic / model-based clustering	Bayesian latent variable / finite mixture model
Основополагающий источник	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.
Другие названия	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Связанные	6	6
Сводка	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.