Global Sensitivity Analysis: The Primer

By Andrea Saltelli, Ratto Marco, A. Terry, Campolongo Francesca, Cariboni Jessica, G. Debora, Saisana Michaela, Tarantola Stefano. 2008

5,418 citations recorded by the source corpus.

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Citations in the corpus

• Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “Saddle River, NJ Saltelli A et al. (2008) Global sensitivity analysis: the primer.”
• Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models (2009, methodology): “The use of these measures is reviewed in [6, 14].”
• Derivative based global sensitivity measures (2014, methodology): “rs are varied as well and thus they account for interactions between variables and do not depend on the choice of a nominal point. Reviews of dierent global SA methods can be found in Saltelli et al [30] and Sobol and Kucherenko [37]. The method of global sensitivity indices suggested by Sobol [33, ”
• Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems (2009, methodology): “We can use principal component analysis (PCA) to quantify the sensitivity of the system (Saltelli et al. , 2008).”
• SALib: An open-source Python library for Sensitivity Analysis (2017, methodology): “…Saltelli, Tarantola, and Chan (1999)), Delta Moment-Independent Measure (E. Borgonovo 2007, Plischke, Borgonovo, and Smith (2013)) Derivative-based Global Sensitivity Measure (DGSM) (Sobol’ and Kucherenko 2009) , and Fractional Factorial Sensitivity Analysis (Andrea Saltelli et al. 2008) methods.”
• A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, methodology): “In addition, important information about the influence of the inputs on the model outputs can be obtained via global sensitivity analysis, e.g. variance based methods [106,107].”
• A Survey of the State of Explainable AI for Natural Language Processing (2020, background): “This is closely related to older concepts such as sensitivity (Saltelli et al., 2008).”
• Sensitivity analysis for multidimensional and functional outputs (2013, background): “We see that the estimated coverages are close to the theoretical level of 95% with a coverage higher than the theoretical one in the Uniform case and lesser in the Gaussian case.”
• Global sensitivity analysis: a flexible and efficient framework with an example from stochastic hydrogeology (2012, background): “Characterizing the impact of parameter variations is known as sensitivity analysis and can be subdivided into local and global analysis [1–3].”