Joint Bayesian model selection and estimation of noisy sinusoidsvia reversible jump MCMC
Andrieu, C.
Doucet, A.
Dept. of Eng., Cambridge Univ.;
This paper appears in: Signal Processing, IEEE Transactions on
Publication Date: Oct 1999
Volume: 47,
Issue: 10
On page(s): 2667-2676
ISSN: 1053-587X
References Cited: 26
CODEN: ITPRED
INSPEC Accession Number: 6380440
Digital Object Identifier: 10.1109/78.790649
Current Version Published: 2002-08-06
Abstract
In this paper, the problem of joint Bayesian model selection and
parameter estimation for sinusoids in white Gaussian noise is addressed.
An original Bayesian model is proposed that allows us to define a
posterior distribution on the parameter space. All Bayesian inference is
then based on this distribution. Unfortunately, a direct evaluation of
this distribution and of its features, including posterior model
probabilities, requires evaluation of some complicated high-dimensional
integrals. We develop an efficient stochastic algorithm based on
reversible jump Markov chain Monte Carlo methods to perform the Bayesian
computation. A convergence result for this algorithm is established. In
simulation, it appears that the performance of detection based on
posterior model probabilities outperforms conventional detection schemes
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