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

Fei Ye and Yifei Wang

Discrete Dynamics in Nature and Society, 2014, vol. 2014, 1-6

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:231704

DOI: 10.1155/2014/231704

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