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Bounding $$\bar d$$-distance by informational divergence: A method to prove measure concentration. (English) Zbl 0865.60017
Summary: There is a simple inequality by Pinsker between variational distance and informational divergence of probability measures defined on arbitrary probability spaces. We shall consider probability measures on sequences taken from countable alphabets, and derive, from Pinsker’s inequality, bounds on $$\overline d$$-distance by informational divergence. Such bounds can be used to prove the “concentration of measure” phenomenon for some nonproduct distributions.

##### MSC:
 60F10 Large deviations 60G70 Extreme value theory; extremal stochastic processes 60G05 Foundations of stochastic processes
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##### References:
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