Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty

Green, PL and Worden, K (2015) Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 373 (2051). 20140405-.

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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.

Item Type:	Article
Uncontrolled Keywords:	nonlinear, system identification, model updating, Bayesian
Depositing User:	Symplectic Admin
Date Deposited:	18 Sep 2015 09:03
Last Modified:	15 Dec 2022 16:08
DOI:	10.1098/rsta.2014.0405
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/2026580

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• Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty. (deposited 18 Sep 2015 09:03) [Currently Displayed]