MAXIMUM-LIKELIHOOD-ESTIMATION FOR HIDDEN MARKOV-MODELS

被引：247
|
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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