An Efficient Bayes Coding Algorithm for the Non-Stationary Source in Which Context Tree Model Varies from Interval to Interval

Koshi Shimada, Shota Saito, Toshiyasu Matsushima

研究成果: Conference contribution

抄録

The context tree source is a source model in which the occurrence probability of symbols is determined from a finite past sequence, and is a broader class of sources that includes i.i.d. and Markov sources. This paper proposes a source model such that its subsequence is generated from a different context tree model. The Bayes code for such sources requires weighting of the posterior probability distributions for the change patterns of the context tree source and all possible context tree models. Therefore, the challenge is how to reduce this exponential order computational complexity. In this paper, we assume a special class of prior probability distribution of change patterns and context tree models, and propose an efficient Bayes coding algorithm whose computational complexity is the polynomial order. A full version of this paper is accessible at: https://arxiv.org/abs/2105.05163.

本文言語English
ホスト出版物のタイトル2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665403122
DOI
出版ステータスPublished - 2021
イベント2021 IEEE Information Theory Workshop, ITW 2021 - Virtual, Online, Japan
継続期間: 2021 10月 172021 10月 21

出版物シリーズ

名前2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings

Conference

Conference2021 IEEE Information Theory Workshop, ITW 2021
国/地域Japan
CityVirtual, Online
Period21/10/1721/10/21

ASJC Scopus subject areas

  • 計算理論と計算数学
  • コンピュータ ネットワークおよび通信
  • 情報システム
  • ソフトウェア

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