New methods for the statistical analysis of Hidden Markov Models

Author

Gerencsér, Lászlo ; Molnár-Sáska, Gábor ; Michaletzky, György ; Tusnády, Gábor ; Vágó, Zsuzsanna

Author_Institution

Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary

Volume

2

fYear

2002

fDate

10-13 Dec. 2002

Firstpage

2272

Abstract

The estimation of Hidden Markov Models has attracted a lot of attention recently. The purpose of this paper is to lay the foundation for a new approach for the analysis of the maximum-likelihood estimation of HMM-s, using representation of HMM-s due to Borkar (1993). A useful connection between the estimation theory of HMM-s and linear stochastic systems is established via the theory of L-mixing processes. The results are potentially useful for deriving strong approximation results, which are in turn applicable to analyze adaptive predictors and change detection methods.

Keywords

hidden Markov models; maximum likelihood estimation; statistical analysis; stochastic systems; Doeblin-condition; Hidden Markov Models; L-mixing processes; estimation theory; maximum-likelihood estimation; random transformations; stochastic systems; Automation; Filters; Hidden Markov models; Markov processes; Mathematics; Maximum likelihood estimation; Stability; State-space methods; Statistical analysis; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2002, Proceedings of the 41st IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-7516-5

Type

conf

DOI

10.1109/CDC.2002.1184870

Filename

1184870