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Maximum-likelihood estimation for hidden Markov models

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  • Leroux, Brian G.

Abstract

Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximum-likelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximum-likelihood estimators is proved. Also, the conclusion of the Shannon-McMillan-Breiman theorem on entropy convergence is established for hidden Markov models.

Suggested Citation

  • Leroux, Brian G., 1992. "Maximum-likelihood estimation for hidden Markov models," Stochastic Processes and their Applications, Elsevier, vol. 40(1), pages 127-143, February.
  • Handle: RePEc:eee:spapps:v:40:y:1992:i:1:p:127-143
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