手法を比較

	ベイズ的多次元尺度構成法 (BMDS)	ベイズ潜在クラス分析（BLCA）
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	2001	1990s–2000s
提唱者	Oh & Raftery	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
種類	Bayesian latent-space dimensionality reduction	Bayesian latent variable / finite mixture 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.	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.
別名	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
関連	6	6
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 latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way.