পদ্ধতির তুলনা করুন

	কুলব্যাক-লাইবলার ডাইভারজেন্স	জেমসেন-শ্যানন ডাইভারজেন্স
ক্ষেত্র	সিদ্ধান্ত গ্রহণ	সিদ্ধান্ত গ্রহণ
পরিবার	MCDM	MCDM
উদ্ভবের বছর	1951	1991
প্রবর্তক	Solomon Kullback and Richard Leibler	J. Lin
ধরন	Asymmetric probability distribution dissimilarity	Symmetric probability distribution dissimilarity
মৌলিক উৎস	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.
অপর নাম	KL divergence, relative entropy, information divergence	JS divergence, symmetric KL divergence, JS distance
সম্পর্কিত	2	2
সারসংক্ষেপ	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.