方法对比

	贝叶斯多维尺度分析 (BMDS)	贝叶斯潜在类别分析 (Bayesian Latent Class Analysis, BLCA)
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	2001	1990s–2000s
提出者	Oh & Raftery	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
类型	Bayesian latent-space dimensionality reduction	Bayesian latent variable / finite mixture model
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.	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.
别名	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
相关	6	6
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 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.