TY - GEN
T1 - Information bounds for decentralized sequential detection
AU - Mei, Yajun
PY - 2006
Y1 - 2006
N2 - The main purpose of this paper is to develop an asymptotic theory for the decentralized sequential hypothesis testing problems under the frequentist framework. Sharp asymptotic bounds on the average sample numbers or sample sizes of sequential or fixed-sample tests are provided in the decentralized decision systems in different scenarios subject to error probabilities constraints. Asymptotically optimal tests are offered in the system with full local memory. Optimal binary quantizers are also studied in the case of additive Gaussian sensor noises.
AB - The main purpose of this paper is to develop an asymptotic theory for the decentralized sequential hypothesis testing problems under the frequentist framework. Sharp asymptotic bounds on the average sample numbers or sample sizes of sequential or fixed-sample tests are provided in the decentralized decision systems in different scenarios subject to error probabilities constraints. Asymptotically optimal tests are offered in the system with full local memory. Optimal binary quantizers are also studied in the case of additive Gaussian sensor noises.
UR - http://www.scopus.com/inward/record.url?scp=39049087731&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=39049087731&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2006.262133
DO - 10.1109/ISIT.2006.262133
M3 - Conference contribution
AN - SCOPUS:39049087731
SN - 1424405041
SN - 9781424405046
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 2647
EP - 2651
BT - Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
T2 - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Y2 - 9 July 2006 through 14 July 2006
ER -