Exact filtering in semi-markov jumping system

Noufel Abbassi, Wojciech Pieczynski

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

Abstract

The classical hidden linear Gaussian system allows one to use the classical Kalman filter, which calculates some distributions of interest with linear complexity in number of observations. However, such calculations become impossible when adding a Markov jump process. The aim of the paper is to propose two new hidden models with Markov and semi-Markov jump processes in which the exact computation of the Kalman filter is feasible with linear complexity in number of observations.

Original languageEnglish
Title of host publicationComputational Methods in Science and Engineering - Advances in Computational Science, Lectures Presented at the Int. Conference on Computational Methods in Science and Engineering 2008, ICCMSE 2008
Pages1-4
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008 - Hersonissos, Crete, Greece
Duration: 25 Sept 200830 Sept 2008

Publication series

NameAIP Conference Proceedings
Volume1148 2
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008
Country/TerritoryGreece
CityHersonissos, Crete
Period25/09/0830/09/08

Keywords

  • Bayesian segmentation
  • Hidden Markov models
  • Kalman filtering
  • Markov jumps
  • semi-Markov jumps
  • triplet Markov models

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