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
    EventISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology - Cairo
    Duration: 2007 Dec 152007 Dec 18

    Other

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

    Fingerprint

    Trees (mathematics)

    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

    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] https://doi.org/10.1109/ISSPIT.2007.4458049

    A class of prior distributions on context tree models and an efficient algorithm of the Bayes codes assuming it. / Matsushima, Toshiyasu; Hirasawa, Shigeich.

    ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology. 2007. p. 938-941 4458049.

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

    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., 4458049, pp. 938-941, ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology, Cairo, 07/12/15. https://doi.org/10.1109/ISSPIT.2007.4458049
    Matsushima T, Hirasawa S. 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. 2007. p. 938-941. 4458049 https://doi.org/10.1109/ISSPIT.2007.4458049
    Matsushima, Toshiyasu ; Hirasawa, Shigeich. / A class of prior distributions on context tree models and an efficient algorithm of the Bayes codes assuming it. ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology. 2007. pp. 938-941
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