Bayesian Treatment of Spatially-Varying Parameter Estimation Problems via Canonical BUS

  • Sadegh RahrovaniEmail author
  • Siu-Kiu Au
  • Thomas Abrahamsson
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


The inverse problem of identifying spatially-varying parameters, based on indirect/incomplete experimental data, is a computationally and conceptually challenging problem. One issue of concern is that the variation of the parameter random field is not known a priori, and therefore, it is typical that inappropriate discretization of the parameter field leads to either poor modelling (due to modelling error) or ill-condition problem (due to the use of over-parameterized models). As a result, classical least square or maximum likelihood estimation typically performs poorly. Even with a proper discretization, these problems are computationally cumbersome since they are usually associated with a large vector of unknown parameters. This paper addresses these issues, through a recently proposed Bayesian method, called Canonical BUS. This algorithm is considered as a revisited formulation of the original BUS (Bayesian Updating using Structural reliability methods), that is, an enhancement of rejection approach that is used in conjunction with Subset Simulation rare-event sampler. Desirable features of CBUS to treat spatially-varying parameter inference problems have been studied and performance of the method to treat real-world applications has been investigated. The studied industrial problem originates from a railway mechanics application, where the spatial variation of ballast bed is of our particular interest.


Bayesian methodology Bayesian updating using structural reliability methods (BUS) Subset simulation (SS) Stochastic simulation Rare-event sampler 


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Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  • Sadegh Rahrovani
    • 1
    Email author
  • Siu-Kiu Au
    • 2
  • Thomas Abrahamsson
    • 1
  1. 1.Department of Applied MechanicsChalmers University of TechnologyGothenburgSweden
  2. 2.School of Engineering, University of LiverpoolLiverpoolUK

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