Sensitivity Analysis for Importance Assessment

By Andrea Saltelli. 2002

1,242 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistics and Probability

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Citations in the corpus

A Survey of Methods for Explaining Black Box Models (2018, background): “Sensitivity analysis studies the correlation between the uncertainty in the output of a predictor and that one in its inputs [102].”
Density modification-based reliability sensitivity analysis (2012, methodology): “It aims at explaining the outputs regarding the input uncertainties.[1] We use the ‘global SA’ definition given by Saltelli et al.”
A new uncertainty importance measure (2007, background): “represent the expected percentage of variance that remains if all parameters were known but Xi (Saltelli (2002) [42]).”
Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis (2007, neutral): “However, these may be added as presented in Wee and Wang (1999). This study models a production-inventory system for deteriorating items with time-varying demands and completely backlogged shortages.”
A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis (2012, methodology): “…(also known as importance measure) methods have recently become the preferred approach for sensitivity analysis across a wide range of applications [Saltelli, 2002a, 2002b; Saltelli et al., 1999, 2006, 2010; Frey, 2002; Frey and Patil, 2002; EPA, 2009], including hydrological and land surface…”
A simple and efficient method for global sensitivity analysis based on cumulative distribution functions (2015, positive): “(1), and still easy to implement because, once the sample < yj > j¼1;…;N of output evaluations has been generated, the sensitivity indices can be approximated by simply applying a closed form, algebraic equation over the output samples (Saltelli, 2002a).”
Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices (2017, background): “One definition of sensitivity analysis is “the study of how the uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input” (Saltelli, 2002).”
A review of methods to match building energy simulation models to measured data (2014, methodology): “For detailed descriptions of tools and techniques, refer in particular to the work of Saltelli et al. [117,118] on this particular subject.”
Towards Automatic Concept-based Explanations (2019, methodology): “vatives for many images from the target class. Intuitively, the TCAV score measures how important the concept (represented as a CAV) is for a class prediction by conducting a form of sensitivity test [22]. In order to reject any concepts that were “not learned” by the network, a statistical testing ”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, positive): “This cost can be reduced to C = N (K + 2) by using the sampling and integration mechanism of Saltelli (2002) to estimate first-and total-order variance-based sensitivity indices.”
Sensitivity analysis practices: Strategies for model-based inference (2006, positive): “The best set of prescriptions on the use of SA in modelling is likely the forthcoming Draft Guidance on the Development, Evaluation, and Application of Regulatory Environmental Models, prepared by The EPA Council for Regulatory Environmental Modeling (CREM), where one reads, in a section entitled ‘‘”
Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations (2015, background): “This is known as sensitivity analysis (SA) (Saltelli, 2002). tions of SWI model outputs in the form of probability density functions (PDFs), prediction intervals (PIs), probability of exceedance of critical thresholds, fuzzy numbers, etc. Note that UP analysis is of particular importance in SWI…”
Techno-Economic Assessment Guidelines for CO2 Utilization (2020, background): “Context uncertainty may result from methodological choices in the goal and scope phase (Saltelli, 2002; EC-JRC, 2010; Igos et al., 2019).”
An application of the Kriging method in global sensitivity analysis with parameter uncertainty (2013, methodology): “[4,5] is constructed by ANOVA high dimensional model representation (HDMR).”