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arXiv · 1811.12788

Optimal Uncertainty Quantification on moment class using canonical moments

Jerome Stenger·Fabrice Gamboa·Merlin Keller·Bertrand Iooss
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Abstract

We gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. We evaluate the maximum quantile over a class of distributions defined only by constraints on their moments. The methodology is based on the theory of canonical moments that appears to be a well-suited framework for practical optimization.

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BibTeXRIS

Jerome Stenger, Fabrice Gamboa, Merlin Keller, Bertrand Iooss. 2018-11-30. Optimal Uncertainty Quantification on moment class using canonical moments. https://arxiv.org/abs/1811.12788

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