Usporedite metode

	Bayesijanska analiza klastera	Analiza latentnih klasa (LCA)
Područje	Statistika	Statistika
Obitelj	Latent structure	Latent structure
Godina nastanka	1998–2002	1950s–1968
Tvorac	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Paul F. Lazarsfeld
Vrsta	Probabilistic / model-based clustering	Latent variable / person-centered classification
Temeljni izvor	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Drugi nazivi	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Srodne	6	6
Sažetak	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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.