Bayesian Treatment of Spatially-Varying Parameter Estimation Problems via Canonical BUS
Paper in proceeding, 2016

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 Bayesian approach, via a recently developed stochastic simulation algorithm, 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 the method and its performance 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 particular interest.

Bayesian methodology

Bayesian updating using structural reliability methods (BUS) Subset simulation (SS)

Stochastic simulation

Rare-event sampler


Sadegh Rahrovani


Siu-Kui Au

University of Liverpool

Thomas Abrahamsson


Model Validation and Uncertainty Quantification, vol 3. Conference Proceedings of the Society for Experimental Mechanics Series. 34th IMAC Conference and Exposition on Structural Dynamics, Orlando, Florida, JAN 25-28, 2016

2191-5652 (eISSN)

Vol. 3 1-13
978-3-319-29753-8 (ISBN)

Subject Categories

Mechanical Engineering

Infrastructure Engineering

Reliability and Maintenance

Probability Theory and Statistics

Areas of Advance



C3SE (Chalmers Centre for Computational Science and Engineering)





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