NUMERICAL SENSITIVITY AND EFFICIENCY IN THE TREATMENT OF EPISTEMIC AND ALEATORY UNCERTAINTY

Eric Chojnacki (1) , Jean Baccou (1, 2) , Sébastien Destercke (1, 3)

1 IRSN - Institut de Radioprotection et de Sûreté Nucléaire
2 LATP - Laboratoire d'Analyse, Topologie, Probabilités
3 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

The treatment of both aleatory and epistemic uncertainty by recent methods often requires an high computational effort. In this abstract, we propose a numerical sampling method allowing to lighten the computational burden of treating the information by means of so-called fuzzy random variables.

Mots clés: uncertainty; numerical sampling; fuzzy random variable; hybrid method

Domaines: Intelligence artificielle [cs.AI]; Autre [cs.OH]; Probabilités [math.PR]

Eric Chojnacki, Jean Baccou, Sébastien Destercke. NUMERICAL SENSITIVITY AND EFFICIENCY IN THE TREATMENT OF EPISTEMIC AND ALEATORY UNCERTAINTY. Fifth International Conference on Sensitivity Analysis of Model Output, 2007, Budapest, Hungary. ⟨irsn-00196663⟩