方法对比

	贝叶斯典型相关分析 (Bayesian CCA)	验证性因子分析（CFA）
领域	统计学	心理测量学
方法族	Latent structure	Latent structure
起源年份	2005-2013	1969
提出者	Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013)	Karl Gustav Jöreskog
类型	Latent variable model / dimensionality reduction	Hypothesis-testing 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.	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
别名	Bayesian CCA, probabilistic CCA, BCCA	CFA, confirmatory FA, measurement model, restricted 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.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.