השוואת שיטות

	ניתוח רכיבים עיקריים בייסיאני (BPCA)	ניתוח גורמים גישוש (EFA)
תחום	סטטיסטיקה	סטטיסטיקה
משפחה	Latent structure	Latent structure
שנת המקור	1999	—
הוגה השיטה	Christopher M. Bishop	—
סוג	Bayesian latent variable / dimension reduction	Latent variable / dimension reduction
מקור מכונן	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.
כינויים	BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA	common factor analysis, açımlayıcı faktör analizi, factor analysis
קשורות	2	4
תקציר	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.