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On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection
2008
IEEE Transactions on Information Theory
We consider the design of systems for sequential decentralized detection, a problem that entails several interdependent choices: the choice of a stopping rule (specifying the sample size), a global decision function (a choice between two competing hypotheses), and a set of quantization rules (the local decisions on the basis of which the global decision is made). This paper addresses an open problem of whether in the Bayesian formulation of sequential decentralized detection, optimal local
doi:10.1109/tit.2008.924647
fatcat:7h43aqnrvrfzzoqtyva7itgqle