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

Green, P.L. and Worden, K. orcid.org/0000-0002-1035-238X (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). ARTN 20140405. ISSN 1364-503X

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: nonlinear; system identification; model updating; Bayesian
Dates:
  • Published: 28 September 2015
  • Accepted: 22 May 2015
  • Published (online): 24 August 2015
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/K003836/1
Depositing User: Symplectic Sheffield
Date Deposited: 08 Aug 2016 15:04
Last Modified: 08 Aug 2016 15:06
Published Version: http://dx.doi.org/10.1098/rsta.2014.0405
Status: Published
Publisher: Royal Society
Refereed: Yes
Identification Number: https://doi.org/10.1098/rsta.2014.0405
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