Variational Bayesian analysis for hidden Markov models

McGrory, Clare A. & Titterington, D. M. (2009) Variational Bayesian analysis for hidden Markov models. Australian & New Zealand Journal of Statistics, 51(2), pp. 227-244.

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The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialised with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the Deviance Information Criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the Deviance Information Criterion provides a further tool for model selection which can be used in conjunction with the variational approach.

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25 citations in Scopus
16 citations in Web of Science®
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ID Code: 14860
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Hidden Markov model, Variational approximation, Deviance Information Criterion (DIC), Bayesian analysis
DOI: 10.1111/j.1467-842X.2009.00543.x
ISSN: 1369-1473
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2009 Blackwell Publishing
Copyright Statement: The definitive version is available on publication at
Deposited On: 15 Sep 2008 00:00
Last Modified: 29 Feb 2012 13:59

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