Title :
Sequential MCMC for Bayesian model selection
Author :
Andrieu, Christophe ; De Freitas, Nando ; Doucet, Arnaud
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
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
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; autoregressive processes; importance sampling; noise; parameter estimation; signal sampling; time series; AR time series; Bayesian computational problems; Markov chain Monte Carlo methods; autoregressive time series; generic algorithm; order estimation; resampling procedure; reversible jump MCMC moves; sequential Bayesian model selection; sequential MCMC; sequential importance sampling; sequential simulation-based method; signal model; signal sampling; Bayesian methods; Computational modeling; Data analysis; Electronic switching systems; Gaussian noise; Identity-based encryption; Monte Carlo methods; Nominations and elections; State estimation; Stochastic processes;
Conference_Titel :
Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Conference_Location :
Caesarea
Print_ISBN :
0-7695-0140-0
DOI :
10.1109/HOST.1999.778709
