Stochastic algorithms for Bayesian model selection of AR processes

Author

Adrieu, C. ; Doucet, Amaud

Author_Institution

Dept. of Eng., Cambridge Univ., UK

fYear

1998

fDate

14-16 Sep 1998

Firstpage

324

Lastpage

327

Abstract

In this paper we address the problem of determining the dimensions of an autoregressive process in a Bayesian framework under various assumptions, including stationarity of the process. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo methods. Their convergence is established and computer simulations are provided, demonstrating the efficiency of the approach adopted

Keywords

Bayes methods; Markov processes; Monte Carlo methods; autoregressive processes; matrix algebra; optimisation; signal processing; stochastic processes; AR processes; Bayesian model selection; Markov chain Monte Carlo methods; autoregressive process; complicated posterior distribution; convergence; integration problems; matrix algebra; optimization problems; process stationarity; signal processing; stochastic algorithms; Autoregressive processes; Bayesian methods; Convergence; Integrated circuit modeling; Monte Carlo methods; Signal processing; Signal processing algorithms; Simulated annealing; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1998. Proceedings., Ninth IEEE SP Workshop on

Conference_Location

Portland, OR

Print_ISBN

0-7803-5010-3

Type

conf

DOI

10.1109/SSAP.1998.739400

Filename

739400