পদ্ধতির তুলনা করুন

	বেয়েশীয় বহুমাত্রিক স্কেলিং (BMDS)	Bayesian EFA
ক্ষেত্র	পরিসংখ্যান	মনোমিতি
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	2001	2004 (Bayesian formulation); factor analysis roots: 1904
প্রবর্তক	Oh & Raftery	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)
ধরন	Bayesian latent-space dimensionality reduction	Probabilistic latent variable model
মৌলিক উৎস	Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044.	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.
অপর নাম	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis
সম্পর্কিত	6	4
সারসংক্ষেপ	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 exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data.