Salīdzināt metodes

	Beijesiešu daudzdimensionālā skalēšana (BMDS)	Bayesiskā klasteru analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2001	1998–2002
Autors	Oh & Raftery	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
Tips	Bayesian latent-space dimensionality reduction	Probabilistic / model-based clustering
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.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
Citi nosaukumi	Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
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 cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.