Sciweavers

TIT
2008

On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection

13 years 3 months ago
On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection
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 the problem of whether in the Bayesian formulation of sequential decentralized detection, optimal local decision functions can be found within the class of stationary rules. We develop an asymptotic approximation to the optimal cost of stationary quantization rules and exploit this approximation to show that stationary quantizers are not optimal in general. We also consider the class of blockwise stationary quantizers, and show that asymptotically optimal quantizers are likelihood-based threshold rules.1
XuanLong Nguyen, Martin J. Wainwright, Michael I.
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TIT
Authors XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
Comments (0)