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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...
Moderate deviation behavior of coding for discrete-memoryless channels is investigated. That is, we consider block codes whose rate converges to the channel capacity from below with increasing block length with a certain rate and examine the best `sub-exponential' decay in the maximal probability of error. We prove that a moderate deviation principle (M.D.P.) holds for all convergence rates between...
Given training sequences generated by two distinct, but unknown distributions sharing a common alphabet, we seek a classifier that can correctly decide whether a third test sequence is generated by the first or second distribution using only the training data. To model `limited learning' we allow the alphabet size to grow and therefore probability distributions to change with the blocklength. We prove...
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