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Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
8th International Multi-Conference on Systems, Signals and Devices (SSD 2011)2011
8th International Multi-Conference on Systems, Signals and Devices (SSD 2011)2011
AbstractAbstract
[en] 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 MetropolisHastings 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.
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Ecole Nationale d'Ingenieurs de Sfax (Tunisia); Philadelphia University (Jordan); Chemnitz University of Technology (Germany); vp; 2011; 6 p; SSD 2011: 8. International Multi-Conference on Systems, Signals and Devices; Sousse (Tunisia); 22-25 Mar 2011; Also available from Ecole Nationale d'Ingenieurs de Sfax, Tunisia (TN); 16 refs., 5 figs.
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