@inproceedings{cf1c9ff179184e75b99f801427823e9a,

title = "Asymptotic refinements in Bayesian distributed detection",

abstract = "The performance of a parallel distributed detection system is investigated as the number of sensors tends to infinity. It is assumed that the i.i.d. sensor data are quantized locally into m-ary messages and transmitted to the fusion center for Bayesian binary hypothesis testing. Large deviations techniques are employed to show that the equivalence of absolutely optimal and best identical-quantizer systems is not limited to error exponents, but extends to the actual Bayes error probabilities up to a multiplicative constant. This is true as long as the two hypotheses are mutually absolutely continuous; no further assumptions, such as boundedness of second moments of the post-quantization log-likelihood ratio, are needed.",

author = "Adrian Papamarcou and Po-Ning Chen",

year = "1993",

month = jan,

day = "1",

doi = "10.1109/ISIT.1993.748326",

language = "English",

isbn = "0780308786",

series = "Proceedings of the 1993 IEEE International Symposium on Information Theory",

publisher = "Publ by IEEE",

booktitle = "Proceedings of the 1993 IEEE International Symposium on Information Theory",

note = "null ; Conference date: 17-01-1993 Through 22-01-1993",

}