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The influence of initial conditions on maximum likelihood estimation of the parameters of a binary hidden Markov model

Author

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  • Dunmur, A. P.
  • Titterington, D. M.

Abstract

The Baum-Welch (EM) algorithm is a familiar tool for calculation of the maximum likelihood estimate of the parameters in hidden Markov chain models. For the particular case of a binary Markov chain corrupted by binary channel noise a detailed study is carried out of the influence that the initial conditions impose on the results produced by the algorithm.

Suggested Citation

  • Dunmur, A. P. & Titterington, D. M., 1998. "The influence of initial conditions on maximum likelihood estimation of the parameters of a binary hidden Markov model," Statistics & Probability Letters, Elsevier, vol. 40(1), pages 67-73, September.
    • Handle: RePEc:eee:stapro:v:40:y:1998:i:1:p:67-73
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    Cited by:

    1. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    2. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    3. Kerekes, Monika, 2009. "Growth miracles and failures in a Markov switching classification model of growth," Discussion Papers 2009/11, Free University Berlin, School of Business & Economics.

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