Optimal Uncertainty Quantification on moment class using canonical moments
Statistics Theory
2018-12-03 v1 Applications
Computation
Methodology
Statistics Theory
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.
Cite
@article{arxiv.1811.12788,
title = {Optimal Uncertainty Quantification on moment class using canonical moments},
author = {Jerome Stenger and Fabrice Gamboa and Merlin Keller and Bertrand Iooss},
journal= {arXiv preprint arXiv:1811.12788},
year = {2018}
}
Comments
21 pages, 9 figures