MAXIMUM-LIKELIHOOD-ESTIMATION FOR HIDDEN MARKOV-MODELS

被引：254

作者：

LEROUX, BG

机构：

来源：

STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 1992年 / 40卷 / 01期

关键词：

MARKOV CHAIN; CONSISTENCY; SUBADDITIVE ERGODIC THEOREM; IDENTIFIABILITY; ENTROPY; KULLBACK-LEIBLER DIVERGENCE; SHANNON-MCMILLAN-BREIMAN THEOREM;

D O I：

10.1016/0304-4149(92)90141-C

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

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页码：127 / 143

页数：17

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