Troughton, PT and Godsill, SJ (1998) Reversible jump sampler for autoregressive time series. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 4. pp. 2257-2260. ISSN 0736-7791Full text not available from this repository.
We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 18:27|
|Last Modified:||26 Apr 2017 03:56|