Bayesian Model Assessment: Methods and Case Studies

  • P. Geyskens
  • A. Der Kiureghian
  • P. Monteiro
Conference paper
Part of the Lecture Notes in Engineering book series (LNENG, volume 76)


A Bayesian framework for model assessment, including an analysis of uncertainties due to small sample size, model inexactness and measurement error, was described in a paper by the first author in the previous IFIP Conference [1]. In this paper, we present computational methods for numerical evaluation of the statistical properties of the model parameters. These include efficient integration and simulation methods aimed at computing the marginal distributions and marginal and joint moments of the uncertain parameters. In the second part of the paper, the proposed methods are employed in evaluating several models recommended by the American Concrete Institute for design of concrete structures. To the authors’ knowledge, these applications are first attempts at rigorous evaluation of some widely used models in structural engineering.


Compressive Strength Probability Density Function Model Uncertainty Joint Moment Structural Reliability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© International Federation for Information Processing, Geneva, Switzerland 1992

Authors and Affiliations

  • P. Geyskens
    • 1
  • A. Der Kiureghian
    • 2
  • P. Monteiro
    • 2
  1. 1.Katholieke Universiteit LeuvenBelgium
  2. 2.Department of Civil EngineeringUniversity of CaliforniaBerkeleyUSA

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