A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria

I. Babuška*, F. Nobile, Raul Tempone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

This work describes a solution to the validation challenge problem posed at the SANDIA Validation Challenge Workshop, May 21-23, 2006, NM. It presents and applies a general methodology to it. The solution entails several standard steps, namely selecting and fitting several models to the available prior information and then sequentially rejecting those which do not perform satisfactorily in the validation and accreditation experiments. The rejection procedures are based on Bayesian updates, where the prior density is related to the current candidate model and the posterior density is obtained by conditioning on the validation and accreditation experiments. The result of the analysis is the computation of the failure probability as well as a quantification of the confidence in the computation, depending on the amount of available experimental data.

Original languageEnglish (US)
Pages (from-to)2517-2539
Number of pages23
JournalComputer Methods in Applied Mechanics and Engineering
Volume197
Issue number29-32
DOIs
StatePublished - May 1 2008

Keywords

  • Bayesian updates
  • Failure probability
  • Model validation
  • Uncertainty quantification

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mechanics

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