방법 비교

	베이즈 다차원 척도법 (BMDS)	베이지안 잠재계층 분석 (Bayesian Latent Class Analysis, BLCA)
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	2001	1990s–2000s
창시자	Oh & Raftery	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
유형	Bayesian latent-space dimensionality reduction	Bayesian latent variable / finite mixture model
원전	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.
별칭	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
관련	6	6
요약	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.