Random balance designs for the estimation of first order global sensitivity indices

By Stefano Tarantola, Debora Gatelli, Thierry Alex Mara. Reliability Engineering & System Safety, 2006

323 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3df54acddf6ed9c1955602c8d81568f043009659

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Citations in the corpus

Ratings and rankings: voodoo or science? (2011, background): “Si can be estimated from design points: Sobol’ (1993); Saltelli (2002); Saltelli et al. (2010); Fourier analysis: Tarantola et al. (2006); Plischke (2010); Xu and Gertner (2011), or others.”
Global sensitivity measures from given data (2013, background): “The Fourier Amplitude Sensitivity Test (FAST) (Cukier et al., 1973; Tarantola et al., 2006; Xu and Gertner, 2007) lowers computational burden at around n model runs for estimating η −15 −10 −5 0 5 10 15 20 0 0.1 0.2 Density function y given x 1 Ishigami, 8192 QMC realizations 0 0.1 0.2 0.3 0.4 0.5…”
A randomized orthogonal array-based procedure for the estimation of first- and second-order Sobol' indices (2015, unknown): “They include Fast Amplitude Sensitivity Test (FAST) [6], Random Balance Design (RBD) [7], Bayesian techniques [8], spectral methods based on polynomial chaos expansion [9], or other metamodel-based techniques [10].”
A Generalized ANOVA Dimensional Decomposition for Dependent Probability Measures (2014, unknown): “…of methods or techniques for calculating the global sensitivity indices of a function of independent variables: the random balance design method [31], state-dependent parameter metamodel [22], Sobol’s method [26], polynomial chaos expansion [30], polynomial dimensional decomposition [19],…”
A methodology for performing global uncertainty and sensitivity analysis in systems biology. (2008, methodology): “PRCC and SRRC appear to be, in general, the most efficient and reliable (giving similar results) among the sampling-based indexes (see (Saltelli & Marivoet, 1990)) while eFAST has proven to be one of the most reliable methods among the variance-based techniques (Saltelli, 2004), although computation”
A Review on Global Sensitivity Analysis Methods (2014, unknown): “Saltelli et al. [79] have extended this technique to compute total Sobol’ indices and Tarantola et al. [90] have coupled FAST with a Random Balance Design.”
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2010, background): “018 Following the recent development of metamodels, which provide cheap emulators of complex and large computational models, new methods have been proposed to compute sensitivity indices [20,23,43,45].”
Generalized Hoeffding-Sobol Decomposition for Dependent Variables -Application to Sensitivity Analysis (2011, unknown): “[38] adapted the Random Balance Designs (RBD) to FAST method for SA (see also recent advances on the subject by Tissot et al.”