MCMC for parameters estimation by Bayesian approach

Author

Ait Saadi, H. ; Ykhlef, Faycal ; Guessoum, Abderrezak

Author_Institution

Dept. de l´Electron., Saad Dahlab Univ., Blida, Algeria

fYear

2011

fDate

22-25 March 2011

Firstpage

1

Lastpage

6

Abstract

This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The Metropolis-Hastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; inference mechanisms; parameter estimation; Bayesian approach; Bayesian inference; MCMC; Markov Chain Monte Carlo methods; Metropolis-Hastings algorithm; parameter estimation; posterior density approximation; Bayesian methods; Convergence; Estimation; Markov processes; Monte Carlo methods; Noise; Proposals; Bayesian approach; MCMC; MMSE; Metropolis-Hastings; dynamic system; parameters estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Signals and Devices (SSD), 2011 8th International Multi-Conference on

Conference_Location

Sousse

Print_ISBN

978-1-4577-0413-0

Type

conf

DOI

10.1109/SSD.2011.5767395

Filename

5767395