AN ASYMPTOTIC ANALYSIS OF BAYESIAN STATE ESTIMATION IN HIDDEN MARKOV MODELS

被引：0

作者：

Yamazaki, Keisuke ^{[1
]}

机构：

[1] Tokyo Inst Technol, Precis & Intelligence Lab, Midori Ku, Yokohama, Kanagawa 227, Japan

来源：

2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2011年

关键词：

Hidden Markov models; latent variable estimation; Bayes statistics; asymptotic analysis; algebraic geometry;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Hidden Markov models are widely used for modeling underlying dynamics of sequence data. The accurate hidden state estimation is one of central issues on practical application since the dynamics is described as a sequence of hidden states. However, while there are many studies on parameter estimation, mathematical properties of the hidden state estimation have not been clarified yet. The present paper analyzes the accuracy of a Bayesian hidden state estimation and shows that the dominant order of an error function depends on redundancy of states.

引用

页数：6

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