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Lookup NU author(s): Professor Richard Boys,
Dr Daniel Henderson
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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.
Author(s): Henderson DA; Boys RJ
Publication type: Article
Publication status: Published
Journal: Scientific Programming
Print publication date: 01/01/2002
ISSN (print): 1058-9244
Publisher: IOS Press