A Strong Data Processing Inequality for Thinning Poisson Processes and Some ApplicationsReport as inadecuate




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1 ETIS - Equipes Traitement de l-Information et Systèmes

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.





Author: Ligong Wang -

Source: https://hal.archives-ouvertes.fr/



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