Optimal Uncertainty Quantification of a risk measurement from a thermalhydraulic code using Canonical Moments
Abstract
We study an industrial computer code related to nuclear safety. A major topic of interest is to assess the uncertainties tainting the results of a computer simulation. In this work we gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. To that extent, we evaluate the maximum quantile over a class of distributions defined only by constraints on their moments. Two options are available when dealing with such complex optimization problems: one can either optimize under constraints; or preferably, one should reformulate the objective function. We identify a well suited parameterization to compute the optimal quantile based on the theory of canonical moments. It allows an effective, free of constraints, optimization.
 Publication:

arXiv eprints
 Pub Date:
 January 2019
 arXiv:
 arXiv:1901.07903
 Bibcode:
 2019arXiv190107903S
 Keywords:

 Statistics  Methodology;
 Mathematics  Probability;
 Statistics  Applications
 EPrint:
 arXiv admin note: substantial text overlap with arXiv:1811.12788