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A novel method for decoding any high-order hidden Markov model
YE Fei 1, WANG Yi-Fei 2
1.Computational Experiment Center for Social Science, Nanjing University, Nanjing 210093;School of Mathematics and Computer, Tongling University, Tongling 244061
2. Department of Mathematics, Shanghai University, Shanghai 200444
*Correspondence author
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Funding: 中国博士后科学基金资助项目（No.No. 2014M551565）
Opened online: 2 June 2014
Accepted by: none
Citation: YE Fei, WANG Yi-Fei.A novel method for decoding any high-order hidden Markov model[OL]. [ 2 June 2014] http://en.paper.edu.cn/en_releasepaper/content/4598699
 
 
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.
Keywords:Probability and mathematical statistics; High-order hidden Markov model; Decoding problem; Model reduction method; Hadar's transformation; Viterbi algorithm.
 
 
 

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