Bayesian Inference for Complex Computer Models

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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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

Chapter Chapter 5 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 157-184 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-6944-6_5

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