Salīdzināt metodes

	Beiziešu daudzfaktoru korespondences analīze (BMCA)	Latent Class Analysis (LCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2000s–2010s	1950s–1968
Autors	Extension of MCA (Benzecri, 1973) with Bayesian inference	Paul F. Lazarsfeld
Tips	Bayesian dimension reduction for categorical data	Latent variable / person-centered classification
Pirmavots	Greenacre, M. & Blasius, J. (Eds.) (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1584886280	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Citi nosaukumi	Bayesian MCA, BMCA, Bayesian multiway correspondence analysis, Bayesian categorical dimension reduction	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Saistītās	5	6
Kopsavilkums	Bayesian Multiple Correspondence Analysis extends classical MCA by embedding the geometric decomposition of categorical data tables within a Bayesian probabilistic framework, enabling principled uncertainty quantification around category coordinates, dimension selection via marginal likelihood, and incorporation of prior knowledge about variable relationships.	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.