Andrieu, C.
De Freitas, N.
Doucet, A.
Dept. of Eng., Cambridge Univ.;
This paper appears in: Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Publication Date: 1999
On page(s): 130-134
Meeting Date: 06/14/1999 - 06/16/1999
Location: Caesarea, Israel
ISBN: 0-7695-0140-0
References Cited: 16
INSPEC Accession Number: 6430574
Digital Object Identifier: 10.1109/HOST.1999.778709
Posted online: 2002-08-06 23:02:41.0
Abstract
In this paper, we address the problem of sequential Bayesian model
selection. This problem does not usually admit any closed-form
analytical solution. We propose here an original sequential
simulation-based method to solve the associated Bayesian computational
problems. This method combines sequential importance sampling, a
resampling procedure and reversible jump MCMC (Markov chain Monte Carlo)
moves. We describe a generic algorithm and then apply it to the problem
of sequential Bayesian model order estimation of autoregressive (AR)
time series observed in additive noise
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