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| Bayesian Multidimensionale Skalierung (BMDS)× | Bayesian Principal Component Analysis (BPCA)× | |
|---|---|---|
| Fachgebiet | Statistik | Statistik |
| Familie | Latent structure | Latent structure |
| Entstehungsjahr≠ | 2001 | 1999 |
| Urheber≠ | Oh & Raftery | Christopher M. Bishop |
| Typ≠ | Bayesian latent-space dimensionality reduction | Bayesian latent variable / dimension reduction |
| 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 ↗ | Bishop, C. M. (1999). Bayesian PCA. In M. S. Kearns, S. A. Solla & D. A. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 382–388). MIT Press. link ↗ |
| Aliasnamen | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA |
| Verwandt≠ | 6 | 2 |
| 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 principal component analysis embeds probabilistic PCA within a Bayesian framework, placing priors over the loading matrix so that irrelevant components are automatically pruned. It handles missing data naturally and provides principled uncertainty estimates for both the latent scores and the dimensionality of the representation. |
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