CUED Publications database

Bayesian model selection for time series using Markov chain Monte Carlo

Troughton, PT and Godsill, SJ (1997) Bayesian model selection for time series using Markov chain Monte Carlo. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5. pp. 3733-3736. ISSN 0736-7791

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Abstract

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:58
Last Modified: 27 Nov 2014 19:25
DOI: