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Sensitivity analysis (SA) of model output investigates how the predictions of a model are related to input parameters. In particular, Monte Carlo-based SA attempts to explain the uncertainty in model output by apportioning the total output uncertainty to the uncertainties of individual input parameters. Techniques employed in the existing literature were affected by severe limitations in the presence of non-monotonic relationships between input and output. The search for better SA methods was pursued with reference to their reproducibility and accuracy. The former is a measure of how well SA predictions are replicated when repeating the analysis on independent samples taken from the same input parameter space. The latter deals with the correctness of the SA results. The present note continues and completes the analysis of the performance of IFFD with respect to the two requirements. IFFD was found to generate highly reproducible results for sufficiently large sample sizes. It exceeded the capability of linear methods by detecting quadratic effects in the relationship between input parameters and model predictions, but had difficulty in dealing with higher order effects.

Additional information

Authors: SALTELLI A, JRC Ispra (IT);ANDRES T H, Atomic Energy of Canada Ltd., Whiteshell Nuclear Research Establishment, Manitoba (CA);HOMMA T, Japan Atomic Energy Research Institute, Tokai-Mura, Ibaraki (JP)
Bibliographic Reference: Article: Computational Statistics and Data Analysis, Vol. 20 (1995) pp. 387-407
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