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 0736-7791Full text not available from this repository.
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.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Unnamed user with email firstname.lastname@example.org|
|Date Deposited:||09 Dec 2016 17:52|
|Last Modified:||23 Jan 2017 05:02|