TY - GEN

T1 - An Efficient Bayes Coding Algorithm for the Non-Stationary Source in Which Context Tree Model Varies from Interval to Interval

AU - Shimada, Koshi

AU - Saito, Shota

AU - Matsushima, Toshiyasu

N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported in part by JSPS KAKENHI Grant Numbers JP17K06446, JP19K04914, and JP19K14989.
Publisher Copyright:
© 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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U2 - 10.1109/ITW48936.2021.9611430

DO - 10.1109/ITW48936.2021.9611430

M3 - Conference contribution

AN - SCOPUS:85123412706

T3 - 2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings

BT - 2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Information Theory Workshop, ITW 2021

Y2 - 17 October 2021 through 21 October 2021

ER -