Comparer des méthodes

	Analyse en Composantes Principales Bayésienne (BPCA)	Analyse factorielle exploratoire (AFE)
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1999	—
Auteur d'origine	Christopher M. Bishop	—
Type	Bayesian latent variable / dimension reduction	Latent variable / dimension reduction
Source fondatrice	Bishop, C. M. (1999). Bayesian PCA. In M. S. Kearns, S. A. Solla & D. A. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 382–388). MIT Press.	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Alias	BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA	common factor analysis, açımlayıcı faktör analizi, factor analysis
Apparentées	2	4
Résumé	Bayesian principal component analysis embeds probabilistic PCA within a Bayesian framework, placing priors over the loading matrix so that irrelevant components are automatically pruned. It handles missing data naturally and provides principled uncertainty estimates for both the latent scores and the dimensionality of the representation.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.