Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
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Summary
Global sensitivity indices for rather complex mathematical models can be efficiently computed by Monte Carlo methods for estimating the influence of individual variables or groups of variables on the model output.
- Type
- article
- Published
- 2001-02-15
- Cited by
- 5,407
- References
- 9
- OpenAlex
- https://openalex.org/W2101589741
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:122859424
Keywords
Monte Carlo method, Sensitivity (control systems), Monte Carlo molecular modeling, Monte Carlo method in statistical physics, Hybrid Monte Carlo
References
- About the use of rank transformation in sensitivity analysis of model output
- On the use of variance reducing multipliers in Monte Carlo computations of a global sensitivity index
- Nonlinear sensitivity analysis of multiparameter model systems
- Importance measures in global sensitivity analysis of nonlinear models
- A method for the non-linear analysis of the sensitivity of mathematical models
- Sensitivity Measures, ANOVA-like Techniques and the Use of Bootstrap
- On quasi-Monte Carlo integrations
- Nonlinear sensitivity analysis of multiparameter model systems
- Monte Carlo estimation of uncertainty contributions from several independent multivariate sources.
- Sensitivity Estimates for Nonlinear Mathematical Models
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