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

Philos Trans A Math Phys Eng Sci. 2015 Sep 28;373(2051):20140405. doi: 10.1098/rsta.2014.0405.


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.

Keywords: Bayesian; model updating; nonlinear; system identification.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review