Unsupervised signal restoration using Copulas and pairwise Markov chains

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Abstract

This work is about the statistical restoration of hidden discrete signals. The problem we deal with is how to take into account, in recent pairwise and triplet Markov chain context, complex noises that can be non-Gaussian, correlated, and of class-varying nature. We propose to solve this modeling problem using Copulas. The interest of the new modeling is validated by experiments performed in supervised and unsupervised context. In the latter, all parameters are estimated from the only observed data by an original method.

Original languageEnglish
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Pages102-105
Number of pages4
ISBN (Electronic)0780379977
DOIs
Publication statusPublished - 1 Jan 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: 28 Sept 20031 Oct 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2003-January

Conference

ConferenceIEEE Workshop on Statistical Signal Processing, SSP 2003
Country/TerritoryUnited States
CitySt. Louis
Period28/09/031/10/03

Keywords

  • Bayesian methods
  • Context modeling
  • Hidden Markov models
  • Parameter estimation
  • Probability
  • Signal processing
  • Signal restoration
  • Stochastic processes
  • Writing
  • Zinc

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