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On determining the order of Markov dependence of an observed process governed by a hidden Markov model

Lookup NU author(s): Professor Richard Boys, Dr Daniel Henderson

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Abstract

This paper describes a Bayesian approach to determining the order of a finite state Markov chain whose transition probabilities are themselves governed by a homogeneous finite state Markov chain. It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models. The method we describe uses a Markov chain Monte Carlo algorithm to obtain samples from the (posterior) distribution for both the order of Markov dependence in the observed sequence and the other governing model parameters. These samples allow coherent inferences to be made straightforwardly in contrast to those which use information criteria. The methods are illustrated by their application to both simulated and real data sets.

Publication metadata

Author(s): Henderson DA; Boys RJ

Publication type: Article

Publication status: Published

Journal: Scientific Programming

Year: 2002

Volume: 10

Issue: 3

Pages: 241-251

Print publication date: 01/01/2002

ISSN (print): 1058-9244

ISSN (electronic):

Publisher: IOS Press

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