Comparer des méthodes

	Divergence de Kullback-Leibler	Divergence de Jensen-Shannon
Domaine	Prise de décision	Prise de décision
Famille	MCDM	MCDM
Année d'origine	1951	1991
Auteur d'origine	Solomon Kullback and Richard Leibler	J. Lin
Type	Asymmetric probability distribution dissimilarity	Symmetric probability distribution dissimilarity
Source fondatrice	Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.	Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.
Alias	KL divergence, relative entropy, information divergence	JS divergence, symmetric KL divergence, JS distance
Apparentées	2	2
Résumé	Kullback-Leibler divergence, also called relative entropy or information divergence, measures the asymmetric difference between two probability distributions. Introduced by Solomon Kullback and Richard Leibler in 1951, this information-theoretic measure quantifies how one probability distribution diverges from a reference distribution, ranging from 0 (identical distributions) to infinity. It is foundational in information theory, machine learning, and decision-making with probabilistic frameworks.	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.