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Metamodeling of Model Evidence

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Model Validation and Uncertainty Quantification, Volume 3

Abstract

The Bayesian model evidence can be used for model selection in model updating problems. The evidence is calculated by computing the integral of the prior multiplied by the likelihood probability density functions (PDF). This integral is usually solved numerically using Monte Carlo integration techniques. A large number of samples of the likelihood and prior should be computed to implement such algorithms. This process might not be achievable when the model is computationally expensive. Researchers have proposed the use of metamodels to replace the computationally expensive model to solve this problem. In this paper we propose a different strategy. The evidence integrand is replaced by a metamodel, simplifying the model selection process because the scale parameter of the metamodel approximates the value of the integral and no Monte Carlo integration is needed. The proposed technique is explored using a set of numerical problems.

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Authors and Affiliations

  1. University of South Carolina, 300 Main St., Columbia, SC, 29208, USA

    Ramin Madarshahian & Juan M. Caicedo

Authors
  1. Ramin Madarshahian
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  2. Juan M. Caicedo
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Corresponding author

Correspondence to Ramin Madarshahian .

Editor information

Editors and Affiliations

  1. Civil Engineering, Clemson University, CLEMSON, South Carolina, USA

    Sez Atamturktur

  2. Kohler Company, Kohler, USA

    Tyler Schoenherr

  3. Tufts University, Medford, Massachusetts, USA

    Babak Moaveni

  4. University of Thessaly, Thessaly, Greece

    Costas Papadimitriou

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© 2016 The Society for Experimental Mechanics, Inc.

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Madarshahian, R., Caicedo, J.M. (2016). Metamodeling of Model Evidence. In: Atamturktur, S., Schoenherr, T., Moaveni, B., Papadimitriou, C. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29754-5_30

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  • DOI: https://doi.org/10.1007/978-3-319-29754-5_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29753-8

  • Online ISBN: 978-3-319-29754-5

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