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

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  • Fei Ye
  • Yifei Wang

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.

Suggested Citation

  • Fei Ye & Yifei Wang, 2014. "A Novel Method for Decoding Any High-Order Hidden Markov Model," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-6, November.
    • Handle: RePEc:hin:jnddns:231704
    DOI: 10.1155/2014/231704
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    Cited by:

    1. Wang, Guo-gang & Gan, Zong-liang & Tang, Gui-jin & Cui, Zi-guan & Zhu, Xiu-chang, 2016. "Basic problems solving for two-dimensional discrete 3 × 4 order hidden markov model," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 73-82.
    2. Wang, Guo-gang & Tang, Gui-jin & Gan, Zong-liang & Cui, Zi-guan & Zhu, Xiu-chang, 2016. "Basic problems and solution methods for two-dimensional continuous 3 × 3 order hidden Markov model," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 435-446.

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