Publication: Tensor Algorithms for Advanced Sensitivity Metrics
Tensor Algorithms for Advanced Sensitivity Metrics
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Ballester-Ripoll, R., Paredes, E. G., & Pajarola, R. (2018). Tensor Algorithms for Advanced Sensitivity Metrics. SIAM/ASA Journal on Uncertainty Quantification, 6, 1172–1197. https://doi.org/10.1137/17M1160252
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Following up on the success of the analysis of variance (ANOVA) decomposition and the Sobol indices (SI) for global sensitivity analysis, various related quantities of interest have been defined in the literature, including the effective and mean dimensions, the dimension distribution, and the Shapley values. Such metrics combine up to exponential numbers of SI in different ways and can be of great aid in uncertainty quantification and model interpretation tasks, but are computationally challenging. We focus on surrogate-based sensiti
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- Ballester-Ripoll, Rafael
- Paredes, Enrique G
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Citations
Ballester-Ripoll, R., Paredes, E. G., & Pajarola, R. (2018). Tensor Algorithms for Advanced Sensitivity Metrics. SIAM/ASA Journal on Uncertainty Quantification, 6, 1172–1197. https://doi.org/10.1137/17M1160252
