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Monte Carlo simulation for moment-independent sensitivity analysis · Study

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ArticleMonte Carlo simulation for moment-independent sensitivity analysis

Pengfei Wei2012

Article2012

Monte Carlo simulation for moment-independent sensitivity analysis

Pengfei Wei · Zhenzhou Lü · Xiukai Yuan

120 citations·0/0 files stored·Reliability Engineering & System Safety

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Probabilistic and Robust Engineering Design Simulation Techniques and Applications Gaussian Processes and Bayesian Inference

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References

2009Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index 2002Making best use of model evaluations to compute sensitivity indices 1996Importance measures in global sensitivity analysis of nonlinear models 2002Sensitivity Analysis for Importance Assessment 1973Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory 2006A new uncertainty importance measure 2009Derivative based global sensitivity measures and their link with global sensitivity indices 2005Random balance designs for the estimation of first order global sensitivity indices

Cited by

2014Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations 2020A combined radial basis function and adaptive sequential sampling method for structural reliability analysis 2018An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy 2014Extended Monte Carlo Simulation for Parametric Global Sensitivity Analysis and Optimization

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Details

Year2012

Typearticle

Published2012-09-27

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations120

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2012.09.005

openalex_workW2017983576

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2014

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2017Borgonovo moment independent global sensitivity analysis by Gaussian radial basis function meta-model

2019The Bhattacharyya distance: Enriching the P-box in stochastic sensitivity analysis