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Fault detection in HMM's: A local asymptotic approach

François Le Gland 1 Laurent Mevel 1
1 SIGMA2 - Signal, models, algorithms
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : The problem of detecting a change in the transition probability matrix of a hidden Markov chain is addressed, using the local asymptotic approach. The score function, evaluated at the nominal value, is used as the residual, and is expressed as an additive functional of the extended Markov chain consisting of the hidden state, the observation, the prediction filter and its gradient w.r.t. the parameter. The problem of residual evaluation is solved using available limit theorems on the extended Markov chain, which allow us to replace the original detection problem by the simpler problem of detecting a change in the mean of a Gaussian r.v.
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https://hal.inria.fr/hal-00912074
Contributor : Francois Le Gland <>
Submitted on : Sunday, December 1, 2013 - 10:59:02 PM
Last modification on : Tuesday, June 15, 2021 - 4:21:50 PM

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François Le Gland, Laurent Mevel. Fault detection in HMM's: A local asymptotic approach. Proceedings of the 39th Conference on Decision and Control, Sydney 2000, IEEE--CSS, Dec 2000, Sydney, Australia. pp.4686--4690, ⟨10.1109/CDC.2001.914667⟩. ⟨hal-00912074⟩

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