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Robust Sensitivity Analysis

Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty.

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Robust Sensitivity Analysis — Uncertainty-resistant examination of model output variation under parameter perturbations

Taksonomisk metoderegister · process-pipeline / simulation

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. · ISBN 9780470059975
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214-232. · DOI 10.1016/j.envsoft.2016.02.008

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Same method familyLatin Hypercube Samplingmachine-suggested · Relational suggestion, not evidence.See alsoMONTE-CARLO-SIMULATIONmachine-suggested · Relational suggestion, not evidence.Same method familyUncertainty Quantificationmachine-suggested · Relational suggestion, not evidence.