Bayesian Inverse Problems for Uncertainty Quantification: Prediction with Model Discrepancy and a Verification Framework

05-08-2014 01:30 PM - 05-09-2014 02:30 PM
Jerry McMahan, North Carolina State University, Raleigh
CSMD Seminar Series
Research Office Building (5700), Room F-234
Email: Cory Hauck

Recent work in uncertainty quantification (UQ) has made it feasible to compute the statistical uncertainties for mathematical models in physics, biology, and engineering applications, offering added insight into how the model relates to the measurement data it represents. This talk focuses on two issues related to the reliability of UQ methods for model calibration in practice. The first issue concerns calibration of models having discrepancies with respect to the phenomena they model when these discrepancies violate commonly employed statistical assumptions used for simplifying computation. Using data from a vibrating beam as a case study, I will illustrate how these discrepancies can limit the accuracy of predictive simulation and discuss some approaches for reducing the impact of these limitations. The second issue concerns verifying the accurate implementation of computational algorithms for solving inverse problems in UQ. In this context, verification is particularly important as the nature of the computational results makes detection of subtle implementation errors unlikely. I will present a collaboratively developed computational framework for verification of statistical inverse problem solvers and present examples of its use to verify the Markov Chain Monte Carlo (MCMC) based routines in the QUESO C++ library.

About the Speaker:
Jerry McMahan is a postdoctoral scholar in the Department of Mathematics at North Carolina State University. If you would like to meet Dr. McMahan, please contact Clayton Webster at or Liz Hebert at


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