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Bayesiansk multippel korrespondanseanalyse (BMCA)×Latent Class Analysis (LCA)×
FagfeltStatistikkStatistikk
FamilieLatent structureLatent structure
Opprinnelsesår2000s–2010s1950s–1968
OpphavspersonExtension of MCA (Benzecri, 1973) with Bayesian inferencePaul F. Lazarsfeld
TypeBayesian dimension reduction for categorical dataLatent variable / person-centered classification
Opprinnelig kildeGreenacre, M. & Blasius, J. (Eds.) (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1584886280Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗
AliasBayesian MCA, BMCA, Bayesian multiway correspondence analysis, Bayesian categorical dimension reductionLCA, latent class model, latent categorical analysis, finite mixture of multinomials
Relaterte56
SammendragBayesian 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.
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ScholarGateSammenlign metoder: Bayesian Multiple Correspondence Analysis · Latent Class Analysis. Hentet 2026-06-17 fra https://scholargate.app/no/compare