방법 비교

	베이즈 다차원 척도법 (BMDS)	베이즈 주성분 분석 (BPCA)
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	2001	1999
창시자	Oh & Raftery	Christopher M. Bishop
유형	Bayesian latent-space dimensionality reduction	Bayesian latent variable / dimension reduction
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.	Bishop, C. M. (1999). Bayesian PCA. In M. S. Kearns, S. A. Solla & D. A. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 382–388). MIT Press.
별칭	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA
관련	6	2
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 principal component analysis embeds probabilistic PCA within a Bayesian framework, placing priors over the loading matrix so that irrelevant components are automatically pruned. It handles missing data naturally and provides principled uncertainty estimates for both the latent scores and the dimensionality of the representation.