Abstract
This section aims at presenting an overview of variance-based approaches for global sensitivity analysis. Starting from functional ANOVA, Sobol' indices are first defined and then estimation algorithms are provided. The performance of these algorithms is theorically and practically discussed. The review includes recent results on the topic.
Author supplied keywords
- Effective algorithm for sensitivity indices
- FANOVA
- Fourier amplitude sensitivity test
- Global sensitivity analysis
- Latin hypercube sampling
- Monte carlo sampling
- Orthogonal arrays (OA)
- Polynomial chaos expansion
- Quasi-Monte carlo sampling
- Random balance design
- Replication
- Sampling design
- Sensitivity indices with given data
- Sobol' sensitivity indices
- Spectral methods
- effective dimension
Cite
CITATION STYLE
APA
Prieur, C., & Tarantola, S. (2017). Variance-based sensitivity analysis: Theory and estimation algorithms. In Handbook of Uncertainty Quantification (pp. 1217–1239). Springer International Publishing. https://doi.org/10.1007/978-3-319-12385-1_35
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