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Quantization of Prior Probabilities for Collaborative Distributed Hypothesis Testing
| Citations: | 8 - 6 self |
BibTeX
@MISC{Rhim_quantizationof,
author = {Joong Bum Rhim and Student Member and Lav R. Varshney and Vivek K Goyal and Senior Member},
title = {Quantization of Prior Probabilities for Collaborative Distributed Hypothesis Testing},
year = {}
}
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Abstract
Abstract—This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of agents and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization on mean Bayes risk are compared. It is shown that when agents using diverse quantizers interact to agree on a perceived common risk, the effective number quantization levels is increased. With this collaboration, optimal diverse regular quantization with cells per quantizer performs as well as optimal identical quantization with cells per quantizer. Similar results are obtained for the maximum Bayes risk error criterion. Index Terms—Bayesian hypothesis testing, Bregman divergence, mean Bayes risk minimization, quantization theory, team theory.
Keyphrases
prior probability collaborative distributed hypothesis testing quantization theory similar result optimal diverse regular quantization mean bayes risk team theory binary local decision index term bayesian hypothesis testing diverse quantization performance equivalence bregman divergence identical quantization quantizer performs optimal identical quantization perceived common risk maximum bayes risk error criterion data fusion powerful single agent effective number quantization level mean bayes risk minimization diverse quantizers paper study
