Salīdzināt metodes

	Bayesiskā klasteru analīze	Latent Class Analysis (LCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1998–2002	1950s–1968
Autors	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Paul F. Lazarsfeld
Tips	Probabilistic / model-based clustering	Latent variable / person-centered classification
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.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Citi nosaukumi	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
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.	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.