Stochastic Complexity for Tree Models

被引：0

作者：

Takeuchi, Jun'ichi ^{[1
]}

Barron, Andrew R. ^{[2
]}

机构：

[1] Kyushu Univ, Dept Informat, Fukuoka, Fukuoka, Japan

[2] Yale Univ, Dept Stat, New Haven, CT USA

来源：

2014 IEEE INFORMATION THEORY WORKSHOP (ITW) | 2014年

关键词：

DATA-COMPRESSION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the problem of data compression, gambling and prediction of strings x(n) = x(1)x(2)... x(n) in terms of coding regret, where the tree model is assumed as a target class. We apply the minimax Bayes strategy for curved exponential families to this problem and show that it achieves the minimax regret without restriction on the data strings. This is an extension of the minimax result by (Takeuchi et al. 2013) for models of kth order Markov chains and determines the constant term of the Stochastic Complexity for the tree model.

引用

页码：222 / 226

页数：5

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