Bayes universal coding algorithm for side information context tree models

Toshiyasu Matsushima, Shigeich Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The problem of universal codes with side information is investigated from Bayes criterion. We propose side information context tree models which are an extension of context tree models to sources with side information. Assuming a special class of the prior distributions for side information context tree models, we propose an efficient algorithm of Bayes code for the models. The asymptotic code length of the Bayes codes with side information is also investigated.

Original languageEnglish
Title of host publicationProceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05
Pages2345-2348
Number of pages4
DOIs
Publication statusPublished - 2005 Dec 1
Event2005 IEEE International Symposium on Information Theory, ISIT 05 - Adelaide, Australia
Duration: 2005 Sep 42005 Sep 9

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2005
ISSN (Print)2157-8099

Conference

Conference2005 IEEE International Symposium on Information Theory, ISIT 05
CountryAustralia
CityAdelaide
Period05/9/405/9/9

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics

Cite this

Matsushima, T., & Hirasawa, S. (2005). Bayes universal coding algorithm for side information context tree models. In Proceedings of the 2005 IEEE International Symposium on Information Theory, ISIT 05 (pp. 2345-2348). [1523767] (IEEE International Symposium on Information Theory - Proceedings; Vol. 2005). https://doi.org/10.1109/ISIT.2005.1523767