विधियों की तुलना करें

	बेयसियन प्रिंसिपल कंपोनेंट एनालिसिस (BPCA)	अन्वेषणात्मक कारक विश्लेषण (EFA)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Latent structure	Latent structure
उद्भव वर्ष	1999	—
प्रवर्तक	Christopher M. Bishop	—
प्रकार	Bayesian latent variable / dimension reduction	Latent variable / dimension reduction
मौलिक स्रोत	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.
उपनाम	BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA	common factor analysis, açımlayıcı faktör analizi, factor analysis
संबंधित	2	4
सारांश	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.