Survey of sampling-based methods for uncertainty and sensitivity analysis

By Jon C. Helton, Jay D. Johnson, Cédric J. Sallaberry, C. Storlie. Reliability Engineering & System Safety, 2006

1,277 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/aaca2b45c0723774153e3948211ba341a4b2fea2

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Citations in the corpus

Calculations of Sobol indices for the Gaussian process metamodel (2008, background): “can be useful (Saltelli et al. [24], Kleijnen [12], Helton et al. [ 9 ]).”
Statistical inference for Sobol pick-freeze Monte Carlo method (2013, neutral): “In practice, one has to estimate (in a statistical sense) those indices using a finite sample (of size typically in the order of hundreds of thousands) of evaluations of model outputs.[4] Indeed, many Monte Carlo or quasi Monte Carlo approaches have been developed”
A methodology for performing global uncertainty and sensitivity analysis in systems biology. (2008, background): “This type of uncertainty is termed epistemic (or subjective, reducible, type B uncertainty; see Helton et al., 2006).”
A review of uncertainty analysis in building energy assessment (2018, methodology): “Sensitivity analysis can be applied after presenting the results of output uncertainty analysis (Figure 2) in order to explain how input variables contribute to output variables [139].”
Introduction to Sensitivity Analysis (2008, neutral): “[23, 30, 57].”
A Review on Global Sensitivity Analysis Methods (2014, background): “[32], Badea and Bolado [4], de Rocquigny et al.”
Agent-based Models of Geographical Systems (2012, background): “London: Taylor and Francis. Anselin, L. (1988). Spatial econometrics: Methods and models .”
Arbitrary polynomial chaos expansion method for uncertainty quantification and global sensitivity analysis in structural dynamics (2020, background): “A surge of work has demonstrated that uncertainty quantification (UQ) and 48 sensitivity analysis (SA) are two essential ingredients of quantitative uncertainty 49 management of physical systems subjected to uncertainty [5].”
Reaction Networks for Interstellar Chemical Modelling: Improvements and Challenges (2010, methodology): “Spearman’s rank correlation coefficient is considered more appropriate for nonlinear models (Helton et al. 2006; Dobrijevic et al. 2010), albeit our experience with sensitivity analysis of chemical networks is that the differences between both methods are generally minor.”
Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models (2009, methodology): “After the characterization of this uncertainty, a number of approaches to SA are available, including differential analysis, variance decomposition procedures, Monte Carlo (sampling-based) analysis, and response surface methods [6, 7, 8].”
Sensitivity in risk analyses with uncertain numbers. (2006, methodology): “For example, the sampling-based methods of sensitivity analysis described by Helton et al. (2006b) are especially useful for tracking analyses that match which particular inputs correspond to outcomes of special interest.”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, methodology): “All source codes, i.e., the NetLogo model implementation and the R/RNetLogo scripts, are available in the Supplementary Material. sensitivity analysis methods (e.g., Saltelli et al. 2000, 2004, 2008; Helton et al. 2006; Kleijnen 1995, 2008; Cournède et al. 2013; Gan et al. 2014).”
Variance-based sensitivity analysis of a forest growth model (2012, methodology): “A model-independent global SA technique is preferable for these models (Helton et al., 2006; Nossent et al., 2011; Saltelli, 2000; Saltelli and Annoni, 2010).”