title: Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty.
abstract: In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories.
authors: Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK p.l.green@liverpool.ac.uk.
authors: P L
authors: PL
authors: Green
authors: Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
authors: K
authors: K
authors: Worden
keywords: Bayesian; model updating; nonlinear; system identification