Comparer des méthodes

	Divergence de Jensen-Shannon	Distance de Hellinger
Domaine	Prise de décision	Prise de décision
Famille	MCDM	MCDM
Année d'origine	1991	1909
Auteur d'origine	J. Lin	Ernst Hellinger
Type	Symmetric probability distribution dissimilarity	Symmetric metric for probability distributions
Source fondatrice	Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.	Hellinger, E. (1909). Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die Reine und Angewandte Mathematik, 136, 210-271.
Alias	JS divergence, symmetric KL divergence, JS distance	Bhattacharyya distance, Hellinger metric
Apparentées	2	2
Résumé	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.