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Fast Convergence Identification of Hidden Markov Models using Risk-Sensitive Filters

Date

Authors

Thorne, Jeremy

Moore, John

Publisher

Pergamon-Elsevier Ltd

Abstract

In this paper we derive recursive risk-sensitive filters which may be used for both on-line and off-line identification of hidden Markov models (HMMs). The identification is achieved by first taking risk-sensitive conditional mean estimates of the number

Keywords

Keywords: Computer simulation; Convergence of numerical methods; Mathematical models; Optimization; Parameter estimation; Hidden Markov models (HMM); Risk-sensitive filters; Markov processes Convergence; Hidden Markov models; Identification; Risk-sensitive

URI

http://hdl.handle.net/1885/70462

Collections

ANU Research Publications

Source

Nonlinear Analysis

Type

Journal article

DOI

10.1016/S0362-546X(01)00369-8

Restricted until

2037-12-31

Downloads

File

Description

01_Thorne_Fast_Convergence_2001.pdf (452.65 KB)