MAXIMUM-LIKELIHOOD-ESTIMATION FOR HIDDEN MARKOV-MODELS
MAXIMUM-LIKELIHOOD-ESTIMATION FOR HIDDEN MARKOV-MODELS
复制标题
DOI：
10.1016/0304-4149(92)90141-c
复制
发表时间：
1992-02-01
影响因子：
1.4
通讯作者：
LEROUX, BG
中科院分区：
文献类型：
--
作者：
LEROUX, BG
隐马尔可夫模型假定一组随机变量序列是条件独立的，并给定一组状态变量序列构成一个马尔可夫链。这些模型的最大似然估计可以使用EM算法进行。本文证明了极大似然估计序列的相合性。同时，给出了隐马尔可夫模型熵收敛的Shannon-McMillan-Breiman定理的结论。
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.