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Fundamental Neural Structures, Operations, and Asymptotic Performance Criteria in Decentralized Binary Hypothesis Testing

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P. Papantoni-Kazakos, D. Kazakos and H. Deliç, Proceedings of the IEEE International Symposium on Information Theory, Budapest, Hungary, June 24-28, 1991, p. 331.

Abstract—In this paper, two fundamental distributed decision network structures are considered: the first system consists of finite number of sensors, each collecting asymptotically many data, while the second one employs asymptotically many sensors, each collecting a single datum. For binary hypothesis testing, the Neyman-Pearson criterion is utilized and justified via information theoretic arguments. Asymptotic relative efficiency performance measure is used to establish tradeoffs between the two structures, by comparing the performance characteristics of the decentralized detection systems to their centralized counter-parts.

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