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Bayesian Inference for Complex Computer Models

In: Frontiers of Statistical Decision Making and Bayesian Analysis

Author

Listed:
  • Ming-Hui Chen

    (University of Connecticut, Department of Statistics)

  • Dipak K. Dey

    (University of Connecticut, Department of Statistics)

  • Peter Müller

    (The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics)

  • Dongchu Sun

    (University of Missouri-Columbia, Department of Statistics)

  • Keying Ye

    (University of Texas at San Antonio, Department of Management Science and Statistics, College of Business)

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.

Suggested Citation

  • Ming-Hui Chen & Dipak K. Dey & Peter Müller & Dongchu Sun & Keying Ye, 2010. "Bayesian Inference for Complex Computer Models," Springer Books, in: Ming-Hui Chen & Peter Müller & Dongchu Sun & Keying Ye & Dipak K. Dey (ed.), Frontiers of Statistical Decision Making and Bayesian Analysis, chapter 0, pages 157-184, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-6944-6_5
    DOI: 10.1007/978-1-4419-6944-6_5
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