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Maximum-likelihood estimation for hidden Markov models

1992; Elsevier BV; Volume: 40; Issue: 1 Linguagem: Inglês

10.1016/0304-4149(92)90141-c

1879-209X

Brian G. Leroux,

Bayesian Modeling and Causal Inference

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.