Bayesian Forecasting for Complex Systems Using Computer Simulators

Although computer models are often used for forecasting future outcomes of complex systems, the uncertainties in such forecasts are not usually treated formally. We describe a general Bayesian approach for using a computer model or simulator of a complex system to forecast system outcomes. The appro...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2001-06, Vol.96 (454), p.717-729
Main Authors: Craig, Peter S, Goldstein, Michael, Rougier, Jonathan C, Seheult, Allan H
Format: Article
Language:English
Subjects:
Approximation
Bayes linear methods
Bayesian analysis
Bayesian method
Calibration
Computer based modeling
Computer experiments
Computer modeling
Computers
Covariance
Design
Diagnostics
Exact sciences and technology
Forecasting models
Forecasting standards
History matching
Hydrocarbon reservoir
Mathematics
Modeling
Parametric inference
Probability and statistics
Sciences and techniques of general use
Simulation
Simulations
Statistical analysis
Statistical discrepancies
Statistical forecasts
Statistical models
Statistics
Theory and Methods
Weather forecasting
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Summary:Although computer models are often used for forecasting future outcomes of complex systems, the uncertainties in such forecasts are not usually treated formally. We describe a general Bayesian approach for using a computer model or simulator of a complex system to forecast system outcomes. The approach is based on constructing beliefs derived from a combination of expert judgments and experiments on the computer model. These beliefs, which are systematically updated as we make runs of the computer model, are used for either Bayesian or Bayes linear forecasting for the system. Issues of design and diagnostics are described in the context of forecasting. The methodology is applied to forecasting for an active hydrocarbon reservoir.
ISSN:0162-1459
1537-274X
DOI:10.1198/016214501753168370