手法を比較

	ベイズ的多次元尺度構成法 (BMDS)	ベイズ確認的因子分析 (BCFA)
分野	統計学	心理測定学
系統	Latent structure	Latent structure
提唱年	2001	2007–2012
提唱者	Oh & Raftery	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov
種類	Bayesian latent-space dimensionality reduction	Bayesian latent variable model
原典	Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044.	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232
別名	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA
関連	6	4
概要	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 confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally.