Exponential forgetting and geometric ergodicity in HMM's - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 1997

Exponential forgetting and geometric ergodicity in HMM's

François Le Gland
  • Fonction : Auteur
  • PersonId : 832228
Laurent Mevel

Résumé

We consider a hidden Markov model with multidimensional observations and with misspecification, i.e. the assumed coefficients (transition probability matrix and observation conditional densities) are possibly different from the true coefficients. Under mild assumptions on the coefficients of both the true and the assumed models, we prove that: 1) the prediction filter forgets almost surely their initial condition exponentially fast; and 2) the extended Markov chain, whose components are the unobserved Markov chain, the observation sequence and the prediction filter, is geometrically ergodic, and has a unique invariant probability distribution.
Fichier non déposé

Dates et versions

hal-00912076 , version 1 (20-12-2013)

Identifiants

Citer

François Le Gland, Laurent Mevel. Exponential forgetting and geometric ergodicity in HMM's. Proceedings of the 36th Conference on Decision and Control, San Diego 1997, IEEE--CSS, Dec 1997, San Diego, United States. pp.537-542, ⟨10.1109/CDC.1997.650683⟩. ⟨hal-00912076⟩
118 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More