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| Dywersencja Jensena-Shannona× | Odległość Hellingera× | |
|---|---|---|
| Dziedzina | Podejmowanie decyzji | Podejmowanie decyzji |
| Rodzina | MCDM | MCDM |
| Rok powstania≠ | 1991 | 1909 |
| Twórca≠ | J. Lin | Ernst Hellinger |
| Typ≠ | Symmetric probability distribution dissimilarity | Symmetric metric for probability distributions |
| Źródło pierwotne≠ | Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. DOI ↗ | Hellinger, E. (1909). Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die Reine und Angewandte Mathematik, 136, 210-271. DOI ↗ |
| Inne nazwy≠ | JS divergence, symmetric KL divergence, JS distance | Bhattacharyya distance, Hellinger metric |
| Pokrewne | 2 | 2 |
| Podsumowanie≠ | Jensen-Shannon divergence is a symmetric information-theoretic measure of the difference between two probability distributions. Developed by Jian Lin in 1991 as a refinement to the asymmetric Kullback-Leibler divergence, it overcomes KL's directional limitation by averaging the divergences in both directions. The result is a true metric (satisfying triangle inequality) that ranges from 0 (identical distributions) to 1, making it suitable for symmetric comparison tasks. | Hellinger distance is a symmetric, bounded metric that measures the difference between two probability distributions. Rooted in the work of Ernst Hellinger (1909) and later formalized in statistical divergence by Anil Bhattacharyya (1946), this distance ranges from 0 (identical distributions) to 1. It is a true metric satisfying all mathematical distance properties and is particularly well-suited for comparing probability distributions in a symmetric, numerically stable manner. |
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