A Strong Data Processing Inequality for Thinning Poisson Processes and Some Applications

Abstract : This paper derives a simple strong data processing inequality (DPI) for Poisson processes: after a Poisson process is passed through p-thinning—in which every arrival remains in the process with probability p and is erased otherwise, independently of the other points—the mutual information between the Poisson process and any other random variable is reduced to no more than p times its original value. This strong DPI is applied to prove tight converse bounds in several problems: a hypothesis test with communication constraints, a mutual information game, and a CEO problem.
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Communication dans un congrès
International Symposium on Information Theory, Jun 2017, Aachen, Germany
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https://hal.archives-ouvertes.fr/hal-01556736
Contributeur : Ligong Wang <>
Soumis le : mercredi 5 juillet 2017 - 14:37:43
Dernière modification le : lundi 17 juillet 2017 - 09:36:55

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  • HAL Id : hal-01556736, version 1

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Ligong Wang. A Strong Data Processing Inequality for Thinning Poisson Processes and Some Applications. International Symposium on Information Theory, Jun 2017, Aachen, Germany. <hal-01556736>

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