NDLI: Optimality of binning for distributed hypothesis testing

Content Provider	IEEE Xplore Digital Library
Author	Rahman, M.S. Wagner, A.B.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA (Rahman, M.S.; Wagner, A.B.)
Abstract	We study a distributed hypothesis testing problem in which data is compressed distributively and the detector seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related problems in distributed source coding, schemes based on random binning are useful and often optimal. For distributed hypothesis testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in binning process. We show that despite this complication, binning is optimal for a class of problems in which the goal is to “test against conditional independence.” We also use this optimality result to give an outer bound for a more general class of instances of the problem.
Starting Page	828
Ending Page	835
File Size	552606
Page Count	8
File Format	PDF
ISBN	9781424482153
e-ISBN	9781424482160
DOI	10.1109/ALLERTON.2010.5706994
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Error probability Source coding Detectors Markov processes Entropy Random variables Testing
Content Type	Text
Resource Type	Article

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