方法对比

	贝叶斯多维尺度分析 (BMDS)	贝叶斯主成分分析 (BPCA)
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	2001	1999
提出者	Oh & Raftery	Christopher M. Bishop
类型	Bayesian latent-space dimensionality reduction	Bayesian latent variable / dimension reduction
开创性文献	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.
别名	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA
相关	6	2
摘要	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 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.