Porovnat metody

	Jensenova-Shannonova divergence	Divergence Kullback-Leiblerova
Obor	Rozhodování	Rozhodování
Rodina	MCDM	MCDM
Rok vzniku	1991	1951
Tvůrce	J. Lin	Solomon Kullback and Richard Leibler
Typ	Symmetric probability distribution dissimilarity	Asymmetric probability distribution dissimilarity
Původní zdroj	Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.	Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.
Další názvy	JS divergence, symmetric KL divergence, JS distance	KL divergence, relative entropy, information divergence
Příbuzné	2	2
Shrnutí	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.	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.