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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 231704, 6 pages
http://dx.doi.org/10.1155/2014/231704
Research Article

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

Fei Ye1,2 and Yifei Wang3

1Computational Experiment Center for Social Science, Nanjing University, Nanjing 210093, China
2School of Mathematics and Computer Science, Tongling University, Tongling, Anhui 244061, China
3Department of Mathematics, Shanghai University, Shanghai 200444, China

Received 9 August 2014; Revised 19 October 2014; Accepted 11 November 2014; Published 23 November 2014

Academic Editor: Weiming Xiang

Copyright © 2014 Fei Ye and Yifei Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.