Limit Theorems in Hidden Markov Models

被引：9

作者：

Han, Guangyue ^{[1
]}

机构：

[1] Univ Hong Kong, Dept Math, Hong Kong, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2013年 / 59卷 / 03期

关键词：

Entropy; hidden Markov models; limit theorem; Shannon-McMillan-Breiman theorem; MAXIMUM-LIKELIHOOD ESTIMATOR; ENTROPY;

D O I：

10.1109/TIT.2012.2226701

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle, and a variant of the Chernoff bound in finite-state hidden Markov models. These limit theorems are of interest in certain areas of information theory and statistics. Particularly, we apply the limit theorems to derive the rate of convergence of the maximum likelihood estimator in finite-state hidden Markov models.

引用

页码：1311 / 1328

页数：18

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