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**Harvard**

Rahrovani, S., Au, S. och Abrahamsson, T. (2016) *Bayesian Treatment of Spatially-Varying Parameter Estimation Problems via Canonical BUS*.

** BibTeX **

@conference{

Rahrovani2016,

author={Rahrovani, Sadegh and Au, Siu-Kui and Abrahamsson, Thomas},

title={Bayesian Treatment of Spatially-Varying Parameter Estimation Problems via Canonical BUS},

booktitle={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},

isbn={978-3-319-29753-8},

pages={1-13},

abstract={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.
},

year={2016},

keywords={Bayesian methodology; Bayesian updating using structural reliability methods (BUS) Subset simulation (SS); Stochastic simulation; Rare-event sampler},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 232386

A1 Rahrovani, Sadegh

A1 Au, Siu-Kui

A1 Abrahamsson, Thomas

T1 Bayesian Treatment of Spatially-Varying Parameter Estimation Problems via Canonical BUS

YR 2016

T2 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

SN 978-3-319-29753-8

SP 1

OP 13

AB 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.

LA eng

DO 10.1007/978-3-319-29754-5_1

LK http://dx.doi.org/10.1007/978-3-319-29754-5_1

OL 30