Salīdzināt metodes

	Bayesiskā klasteru analīze	Beieziešu latentās klases analīze (BLCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1998–2002	1990s–2000s
Autors	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)
Tips	Probabilistic / model-based clustering	Bayesian latent variable / finite mixture model
Pirmavots	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.
Citi nosaukumi	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Saistītās	6	6
Kopsavilkums	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.