手法を比較

	カルバック・ライブラー情報量（Kullback-Leibler divergence）	ジェンセン-シャノンダイバージェンス
分野	意思決定	意思決定
系統	MCDM	MCDM
提唱年	1951	1991
提唱者	Solomon Kullback and Richard Leibler	J. Lin
種類	Asymmetric probability distribution dissimilarity	Symmetric probability distribution dissimilarity
原典	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.
別名	KL divergence, relative entropy, information divergence	JS divergence, symmetric KL divergence, JS distance
関連	2	2
概要	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.