Vertaile menetelmiä

	Jensen-Shannon-divergenssi	Hellinger-etäisyys
Tieteenala	Päätöksenteko	Päätöksenteko
Menetelmäperhe	MCDM	MCDM
Syntyvuosi	1991	1909
Kehittäjä	J. Lin	Ernst Hellinger
Tyyppi	Symmetric probability distribution dissimilarity	Symmetric metric for probability distributions
Alkuperäislähde	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.
Rinnakkaisnimet	JS divergence, symmetric KL divergence, JS distance	Bhattacharyya distance, Hellinger metric
Liittyvät	2	2
Tiivistelmä	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.