A Novel Method for Decoding Any High-Order Hidden Markov Model

被引：6

作者：

Ye, Fei ^{[1
,2
]}

Wang, Yifei ^{[3
]}

机构：

[1] Nanjing Univ, Computat Expt Ctr Social Sci, Nanjing 210093, Jiangsu, Peoples R China

[2] Tongling Univ, Sch Math & Comp Sci, Tongling 244061, Anhui, Peoples R China

[3] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China

来源：

DISCRETE DYNAMICS IN NATURE AND SOCIETY | 2014年 / 2014卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1155/2014/231704

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar's transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.

引用

页数：6

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