Optimal Uncertainty Quantification on moment class using canonical moments

11/30/2018

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by   Jerome Stenger, et al.

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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.