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Maximum-likelihood estimation for hidden Markov models

Brian G. Leroux

Stochastic Processes and their Applications, 1992, vol. 40, issue 1, 127-143

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.

Keywords: Markov; chain; consistency; subadditive; ergodic; theorem; identifiability; entropy; Kullback-Leibler; divergence; Shannon-McMillan-Breiman; theorem (search for similar items in EconPapers)
Date: 1992
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Handle: RePEc:eee:spapps:v:40:y:1992:i:1:p:127-143