Sensitivity Anaysis as an Ingredient of Modeling

By Andrea Saltelli, Stefano Tarantola, Francesca Campolongo. 2000

562 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability

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Citations in the corpus

Joint Monte Carlo and possibilistic simulation for flood damage assessment (2013, unknown): “Typically, it consists of the following steps (Saltelli et al. 2000a, b): (1) identification of sensitive variables target for analysis; (2) selection of the probability distribution function (PDF) with required parameters for each variable; (3) generation of samples based on the PDF information…”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “These operationalize the various SA methods via different programming languages, and include Dakota (Adams et al., 2020) in C ++ , SobolGSA (Kucherenko and Zaccheus, 2020) in C#, MATLAB, and Python, UQLab (Marelli and Sudret, 2014) in MATLAB, OpenTURNS (Baudin et al., 2017) in Python and C ++ , the ”
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (2006, methodology): “Statisticians not conversant with today’s statistical take on DQ should read this book (as well as the Karr, Banks, and Sanil paper), and be stimulated to do important research on DQ. Alan F. KARR National Institute of Statistical Sciences”
Review of surrogate modeling in water resources (2012, methodology): “…optimization algorithms [e.g., Cieniawski et al., 1995; Reed et al., 2003], global sensitivity analysis methods [e.g., Hornberger and Spear, 1981; Saltelli et al., 2000], and any traditional Monte Carlo–based reliability or uncertainty analysis [e.g., Melching et al., 1990; Skaggs and Barry,…”
A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis (2014, methodology): “(5) Assess the robustness of weights in both MCDM techniques in light of GSA results.”