방법 비교

	베이즈 다차원 척도법 (BMDS)	다차원 척도법(MDS)
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	2001	1952–1964
창시자	Oh & Raftery	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
유형	Bayesian latent-space dimensionality reduction	Dimensionality reduction / visualization
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.	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
별칭	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	MDS, metric MDS, non-metric MDS, proximity scaling
관련	6	5
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.	Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data.