方法对比

	贝叶斯主成分分析 (BPCA)	探索性因子分析（EFA）
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	1999	—
提出者	Christopher M. Bishop	—
类型	Bayesian latent variable / dimension reduction	Latent variable / dimension reduction
开创性文献	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.
别名	BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA	common factor analysis, açımlayıcı faktör analizi, factor analysis
相关	2	4
摘要	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.