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Bayesian Inference for Complex Computer Models

  • Ming-Hui Chen
  • Dipak K. Dey
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
Chapter

Abstract

One of the big success stories of Bayesian inference is inference in large complex and highly structured models. A typical example is inference for computer models. Scientists use complex computer models to study the behavior of complex physical processes such as weather forecasting, disease dynamics, hydrology, traffic models, etc. Inference involves three related models, the true system, the complex simulation model and possibly a computationally more efficient emulation model. Appropriate propagation of uncertainties, good choice of emulation models, and calibration of parameters for the emulation model pose challenging inference problems reviewed in this chapter.

Keywords

Computer Model Posterior Distribution Markov Chain Monte Carlo Bayesian Inference Gaussian Process 
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

© Springer New York 2010

Authors and Affiliations

  • Ming-Hui Chen
    • 1
  • Dipak K. Dey
    • 1
  • Peter Müller
    • 2
  • Dongchu Sun
    • 3
  • Keying Ye
    • 4
  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.Department of BiostatisticsThe University of Texas, M. D. Anderson Cancer CenterHoustonUSA
  3. 3.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA
  4. 4.Department of Management Science and Statistics, College of BusinessUniversity of Texas at San AntonioSan AntonioUSA

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