方法对比

	贝叶斯多维尺度分析 (BMDS)	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	2001	2004 (Bayesian formulation); factor analysis roots: 1904
提出者	Oh & Raftery	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)
类型	Bayesian latent-space dimensionality reduction	Probabilistic latent variable model
开创性文献	Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044.	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.
别名	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis
相关	6	4
摘要	Bayesian Multidimensional Scaling places objects in a low-dimensional latent space so that inter-object distances reproduce observed dissimilarities, while a full Bayesian treatment quantifies uncertainty in the coordinates, handles missing proximities naturally, and selects the number of dimensions via model comparison rather than heuristic inspection.	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.