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SUMMARY:Decentralized Quickest Change Detection in Hidden Markov Models fo
 r Sensor Networks - Fuh\, C-D (National Central University\, Taiwan)
DTSTART:20140115T100000Z
DTEND:20140115T103000Z
UID:TALK49920@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:The decentralized quickest change detection problem is studied
  in sensor networks\, where a set of sensors take observations from a hidd
 en Markov model (HMM) and send sensor messages to a fusion center\, which 
 makes a final decision when observations are stopped. It is assumed that t
 he parameter $	heta$ in the HMM model changes from $	heta_0$ to $	heta_1$ 
 at some unknown time. The problem is to determine the policies at the sens
 or and fusion center levels to jointly optimize the detection delay subjec
 t to the average run length (ARL) to false alarm constraint. The primary g
 oal of this paper is to investigate how to choose the best binary stationa
 ry quantizers from the both theoretical and computational viewpoints when 
 a CUSUM-type scheme is used at the fusion center. Further research is also
  given.\n
LOCATION:Seminar Room 1\, Newton Institute
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