Salīdzināt metodes

	Beijesiešu daudzdimensionālā skalēšana (BMDS)	Beieziešu latentās klases analīze (BLCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2001	1990s–2000s
Autors	Oh & Raftery	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
Tips	Bayesian latent-space dimensionality reduction	Bayesian latent variable / finite mixture model
Pirmavots	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.
Citi nosaukumi	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Saistītās	6	6
Kopsavilkums	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.