Bayesian Smoothing Algorithms in Partially Observed Markov Chains - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2006

Bayesian Smoothing Algorithms in Partially Observed Markov Chains

Résumé

Let x = {x n } n∈IN be a hidden process, y = {y n } n∈IN an observed process and r = {r n } n∈IN some auxiliary process. We assume that t = {t n } n∈IN with t n = (x n , r n , y n-1) is a (Triplet) Markov Chain (TMC). TMC are more general than Hidden Markov Chains (HMC) and yet enable the development of efficient restoration and parameter estimation algorithms. This paper is devoted to Bayesian smoothing algorithms for TMC. We first propose twelve algorithms for general TMC. In the Gaussian case, these smoothers reduce to a set of algorithms which include, among other solutions, extensions to TMC of classical Kalman-like smoothing algorithms (originally designed for HMC) such as the RTS algorithms, the Two-Filter algorithms or the Bryson and Frazier algorithm.
Fichier principal
Vignette du fichier
maxent-smoothing.pdf (69.42 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04415416 , version 1 (24-01-2024)

Identifiants

Citer

Boujemaa Ait-El-Fquih, François Desbouvries. Bayesian Smoothing Algorithms in Partially Observed Markov Chains. Bayesian Inference and Maximum Entropy Methods In Science and Engineering, Jul 2006, Paris, France. pp.339-346, ⟨10.1063/1.2423292⟩. ⟨hal-04415416⟩
0 Consultations
1 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More