Bayesian smoothing algorithms in pairwise and triplet Markov chains

B. Ait-El-Fquih*, F. Desbouvries

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

An important problem in signal processing consists in estimating an unobservable process x = {xn}n∈IN from an observed process y = {yn}n∈IN. In Linear Gaussian Hidden Markov Chains (LGHMC), recursive solutions are given by Kalman-like Bayesian restoration algorithms. In this paper, we consider the more general framework of Linear Gaussian Triplet Markov Chains (LGTMC), i.e. of models in which the triplet (x, r, y) (where r = {rn}n∈IN is some additional process) is Markovian and Gaussian. We address fixed-interval smoothing algorithms, and we extend to LGTMC the RTS algorithm by Rauch, Tung and Striebel, as well as the Two-Filter algorithm by Mayne and Fraser and Potter.

Original languageEnglish (US)
Title of host publication2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Book of Abstracts
PublisherIEEE Computer Society
Pages721-726
Number of pages6
ISBN (Print)0780394046, 9780780394049
DOIs
StatePublished - Jan 1 2005
Event2005 IEEE/SP 13th Workshop on Statistical Signal Processing - Bordeaux, France
Duration: Jul 17 2005Jul 20 2005

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2005

Other

Other2005 IEEE/SP 13th Workshop on Statistical Signal Processing
CountryFrance
CityBordeaux
Period07/17/0507/20/05

ASJC Scopus subject areas

  • Signal Processing

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