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- W3016229936 abstract "The problem of quickly diagnosing an unknown change in a stochastic process is studied. We establish novel bounds on the performance of misspecified diagnosis algorithms designed for changes that differ from those of the process, and pose and solve a new robust quickest change diagnosis problem in the asymptotic regime of few false alarms and false isolations. Simulations suggest that our asymptotically robust solution offers a computationally efficient alternative to generalised likelihood ratio algorithms." @default.
- W3016229936 created "2020-04-17" @default.
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- W3016229936 date "2021-02-01" @default.
- W3016229936 modified "2023-10-16" @default.
- W3016229936 title "Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis" @default.
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- W3016229936 doi "https://doi.org/10.1109/tac.2020.2985975" @default.
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