Порівняння методів

	Дивергенція Кульбака-Лейблера	Дивергенція Єнсена-Шеннона
Галузь	Прийняття рішень	Прийняття рішень
Родина	MCDM	MCDM
Рік появи	1951	1991
Автор методу	Solomon Kullback and Richard Leibler	J. Lin
Тип	Asymmetric probability distribution dissimilarity	Symmetric probability distribution dissimilarity
Основоположне джерело	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.
Інші назви	KL divergence, relative entropy, information divergence	JS divergence, symmetric KL divergence, JS distance
Пов'язані	2	2
Підсумок	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.