Compara mètodes

	Divergència de Jensen-Shannon	Distància de Hellinger	Divergència de Kullback-Leibler
Camp	Presa de decisions	Presa de decisions	Presa de decisions
Família	MCDM	MCDM	MCDM
Any d'origen	1991	1909	1951
Autor original	J. Lin	Ernst Hellinger	Solomon Kullback and Richard Leibler
Tipus	Symmetric probability distribution dissimilarity	Symmetric metric for probability distributions	Asymmetric probability distribution dissimilarity
Font seminal	Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.	Hellinger, E. (1909). Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die Reine und Angewandte Mathematik, 136, 210-271.	Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.
Àlies	JS divergence, symmetric KL divergence, JS distance	Bhattacharyya distance, Hellinger metric	KL divergence, relative entropy, information divergence
Relacionats	2	2	2
Resum	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.	Hellinger distance is a symmetric, bounded metric that measures the difference between two probability distributions. Rooted in the work of Ernst Hellinger (1909) and later formalized in statistical divergence by Anil Bhattacharyya (1946), this distance ranges from 0 (identical distributions) to 1. It is a true metric satisfying all mathematical distance properties and is particularly well-suited for comparing probability distributions in a symmetric, numerically stable manner.	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.