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2011
IEEE

Collaboration in Distributed Hypothesis Testing with Quantized Prior Probabilities

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Collaboration in Distributed Hypothesis Testing with Quantized Prior Probabilities
The effect of quantization of prior probabilities in a collection of distributed Bayesian binary hypothesis testing problems over which the priors themselves vary is studied. In a setting with fusion of local binary decisions by majority rule, optimal local decision rules are discussed. Quantization is first considered under the constraint that agents employ identical quantizers. A method for design is presented that exploits an equivalence to a single-agent problem with a different likelihood function; the optimal quantizers are thus different than in the single-agent case. Removing the constraint of identical quantizers is demonstrated to improve performance. A method for design is presented that exploits an equivalence between agents having diverse K-level quantizers and agents having identical (3K − 2)-level quantizers.
Joong Bum Rhim, Lav R. Varshney, Vivek K. Goyal
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where DCC
Authors Joong Bum Rhim, Lav R. Varshney, Vivek K. Goyal
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