Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty.
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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.
SUBMITTER: Green PL
PROVIDER: S-EPMC4549940 | biostudies-literature |
REPOSITORIES: biostudies-literature
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