方法对比

	贝叶斯典型相关分析 (Bayesian CCA)	贝叶斯探索性因子分析 (Bayesian Exploratory Factor Analysis, BEFA)
领域	统计学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2005-2013	2004 (Bayesian formulation); factor analysis roots: 1904
提出者	Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013)	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)
类型	Latent variable model / dimensionality reduction	Probabilistic latent variable model
开创性文献	Bach, F. R. & Jordan, M. I. (2005). A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley.	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.
别名	Bayesian CCA, probabilistic CCA, BCCA	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis
相关	5	4
摘要	Bayesian canonical correlation analysis is a probabilistic generative model that identifies shared latent structure between two or more sets of observed variables. It extends classical CCA by placing priors on model parameters, enabling principled uncertainty quantification, automatic determination of the number of shared dimensions, and robustness when sample sizes are small relative to dimensionality.	Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data.