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Jensen-Shannon divergence: a standalone construct

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Information theory

symmetric and bounded measure of dissimilarity between probability distributions, defined from the Kullback-Leibler divergence of each to their average.

In information theory, Jensen-Shannon divergence refers to symmetric and bounded measure of dissimilarity between probability distributions, defined from the Kullback-Leibler divergence of each to their average. This meaning comes from Lin (1991) in IEEE Transactions on Information Theory.[1]

Unit
Recovery means
Measured by

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Nearby constructs

divergence4 fields · 4 citationsjingle · one word, four constructs information4 fields · 5 citationsjingle · one word, four constructs measure2 fields · 4 citationsjingle · one word, two constructs

References

Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. https://doi.org/10.1109/18.61115

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