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| Bayesian Multidimensionale Skalierung (BMDS)× | Bayesian explorative Faktoranalyse (BEFA)× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Psychometrie |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 2001 | 2004 (Bayesian formulation); factor analysis roots: 1904 |
| Urheber≠ | Oh & Raftery | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) |
| Typ≠ | Bayesian latent-space dimensionality reduction | Probabilistic latent variable model |
| Wegweisende Quelle≠ | Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044. DOI ↗ | Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. link ↗ |
| Aliasnamen | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | 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. |
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