Accounting for both random errors and systematic errors in uncertainty propagation analysis of computer models involving experimental measurements with Monte Carlo methods

Risk Analysis : an Official Publication of the Society for Risk Analysis
Victor R Vasquez, Wallace B Whiting

Abstract

A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of th...Continue Reading

References

Mar 1, 1992·Risk Analysis : an Official Publication of the Society for Risk Analysis·K M ThompsonE A Crouch
Oct 1, 1994·Risk Analysis : an Official Publication of the Society for Risk Analysis·F O Hoffman, J S Hammonds
Nov 18, 1996·Physical Review Letters·M Z Bazant, E Kaxiras

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Citations

Oct 24, 2006·Risk Analysis : an Official Publication of the Society for Risk Analysis·Per SanderTomas Oberg
Apr 15, 2010·Journal of Agromedicine·Terrell BarryMichael O'Malley

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