Gaussian process emulation for second-order Monte Carlo simulations

We consider the use of emulator technology as an alternative method to second-order Monte Carlo (2DMC) in the uncertainty analysis for a percentile from the output of a stochastic model. 2DMC is a technique that uses repeated sampling in order to make inferences on the uncertainty and variability in...

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Bibliographic Details
Published in:Journal of statistical planning and inference 2011-05, Vol.141 (5), p.1838-1848
Main Authors: Johnson, J.S., Gosling, J.P., Kennedy, M.C.
Format: Article
Language:English
Subjects:
Distribution theory
Emulation
Exact sciences and technology
Gaussian process
General topics
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Second-order Monte Carlo
Statistics
Stochastic processes
Uncertainty analysis
Variability
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Summary:We consider the use of emulator technology as an alternative method to second-order Monte Carlo (2DMC) in the uncertainty analysis for a percentile from the output of a stochastic model. 2DMC is a technique that uses repeated sampling in order to make inferences on the uncertainty and variability in a model output. The conventional 2DMC approach can often be highly computational, making methods for uncertainty and sensitivity analysis unfeasible. We explore the adequacy and efficiency of the emulation approach, and we find that emulation provides a viable alternative in this situation. We demonstrate these methods using two different examples of different input dimensions, including an application that considers contamination in pre-pasteurised milk.
ISSN:0378-3758
1873-1171
DOI:10.1016/j.jspi.2010.11.034