विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बायेसियन बहुआयामी स्केलिंग (BMDS)× | मल्टीडायमेंशनल स्केलिंग (MDS)× | |
|---|---|---|
| क्षेत्र | सांख्यिकी | सांख्यिकी |
| परिवार | Latent structure | Latent structure |
| उद्भव वर्ष≠ | 2001 | 1952–1964 |
| प्रवर्तक≠ | Oh & Raftery | Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964) |
| प्रकार≠ | Bayesian latent-space dimensionality reduction | Dimensionality reduction / visualization |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | MDS, metric MDS, non-metric MDS, proximity scaling |
| संबंधित≠ | 6 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
|
|