A class of prior distributions on context tree models and an efficient algorithm of the Bayes codes assuming it

Toshiyasu Matsushima, Shigeich Hirasawa

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

2 Citations (Scopus)

Abstract

The CTW(Context Tree Weighting) algorithm is an efficient universal coding algorithm on context tree models. The CTW algorithm has been interpreted as the non-predictive Bayes coding algorithm assuming a special prior distribution over context tree models. An efficient recursive calculation method using a gathering context tree in the CTWalgorithm is well known. Although there exist efficient recursive algorithms for the Bayes codes assuming a special class of prior distributions, the basic property ofthe prior distribution class has been scarcely investigated. In this paper we show the exact definition of a prior distribution class on context tree models that has the similar property to the class of conjugate priors. We show the posterior distribution is also included in the same distribution class as the prior distribution class. So we can also construct an efficient algorithm ofpredictive Bayes codes on context tree models by using the prior distribution class. Lastly the asymptotic mean code length of the codes IS investigated.

Original languageEnglish
Title of host publicationISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology
Pages938-941
Number of pages4
DOIs
Publication statusPublished - 2007 Dec 1
EventISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology - Cairo, Egypt
Duration: 2007 Dec 152007 Dec 18

Publication series

NameISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology

Conference

ConferenceISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology
CountryEgypt
CityCairo
Period07/12/1507/12/18

Keywords

  • Bayes universal codes
  • Context tree models
  • Prior distribution
  • Source coding

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'A class of prior distributions on context tree models and an efficient algorithm of the Bayes codes assuming it'. Together they form a unique fingerprint.

  • Cite this

    Matsushima, T., & Hirasawa, S. (2007). A class of prior distributions on context tree models and an efficient algorithm of the Bayes codes assuming it. In ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology (pp. 938-941). [4458049] (ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology). https://doi.org/10.1109/ISSPIT.2007.4458049