방법 비교

	베이즈 다차원 척도법 (BMDS)	베이지안 군집 분석
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	2001	1998–2002
창시자	Oh & Raftery	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
유형	Bayesian latent-space dimensionality reduction	Probabilistic / model-based clustering
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.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
별칭	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
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 cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.