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Publication Detail
Approximate bayesian computation for finite element model updating
  • Publication Type:
  • Authors:
    DiazDelao FA, Gomes HM, Mottershead JE
  • Publication date:
  • Pagination:
    301, 306
  • Published proceedings:
    Conference Proceedings of the Society for Experimental Mechanics Series
  • Volume:
  • ISBN-13:
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In recent years, there has been a growing interest in Bayesian model updating methods. The learning process is characterised by estimating the probability distribution of a random parameter within an ensemble of data and prior information. A crucial component of these methods is a marginal likelihood term. However, for most models, an analytical expression cannot be found or can be computationally intractable. A possible solution is to perform likelihood-free inference. Recently, there has been a development of techniques known as Approximate Bayesian Computation (ABC) methods. This work explores the coupling between finite element model updating and ABC, its potential and its limitations.
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