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- W1964107245 abstract "We study Bayesian quickest detection problems where the observations and the underlying change-point are coupled. This setup supersedes classical models that assume independence of the two. We develop several continuous-time formulations of this problem for the cases of Poissonian and Brownian sensors. Our approach to detection uses methods of nonlinear filtering and optimal stopping and lends itself to an efficient numerical scheme that combines particle filtering with Monte Carlo dynamic programming. The developed models and algorithms are illustrated with numerical examples." @default.
- W1964107245 created "2016-06-24" @default.
- W1964107245 creator A5074760129 @default.
- W1964107245 date "2012-12-01" @default.
- W1964107245 modified "2023-09-23" @default.
- W1964107245 title "Bayesian quickest detection with observation-changepoint feedback" @default.
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- W1964107245 doi "https://doi.org/10.1109/cdc.2012.6425853" @default.
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