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| Bayesowskie skalowanie wielowymiarowe (BMDS)× | Skalowanie wielowymiarowe (MDS)× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Latent structure | Latent structure |
| Rok powstania≠ | 2001 | 1952–1964 |
| Twórca≠ | Oh & Raftery | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) |
| Typ≠ | Bayesian latent-space dimensionality reduction | Dimensionality reduction / visualization |
| Źródło pierwotne≠ | 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 ↗ | Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. DOI ↗ |
| Inne nazwy | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | MDS, metric MDS, non-metric MDS, proximity scaling |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data. |
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