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Estimating percentiles of uncertain computer code outputs

Jeremy Oakley

Journal of the Royal Statistical Society Series C, 2004, vol. 53, issue 1, 83-93

Abstract: Summary. A deterministic computer model is to be used in a situation where there is uncertainty about the values of some or all of the input parameters. This uncertainty induces uncertainty in the output of the model. We consider the problem of estimating a specific percentile of the distribution of this uncertain output. We also suppose that the computer code is computationally expensive, so we can run the model only at a small number of distinct inputs. This means that we must consider our uncertainty about the computer code itself at all untested inputs. We model the output, as a function of its inputs, as a Gaussian process, and after a few initial runs of the code use a simulation approach to choose further suitable design points and to make inferences about the percentile of interest itself. An example is given involving a model that is used in sewer design.

Date: 2004
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https://doi.org/10.1046/j.0035-9254.2003.05044.x

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