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

Eric Chojnacki; Jean Baccou; Sébastien Destercke

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 - Institut de recherche en informatique de Toulouse

Abstract : 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.

Keywords : uncertainty numerical sampling fuzzy random variable hybrid method

Document type :

Conference papers

Domain :

Computer Science [cs] / Artificial Intelligence [cs.AI]

Computer Science [cs] / Other [cs.OH]

Mathematics [math] / Probability [math.PR]

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https://hal-irsn.archives-ouvertes.fr/irsn-00196663
Contributor : Sébastien Destercke <>
Submitted on : Thursday, December 13, 2007 - 11:32:19 AM
Last modification on : Friday, October 11, 2019 - 8:22:48 PM
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NUMERICAL SENSITIVITY AND EFFICIENCY IN THE TREATMENT OF EPISTEMIC AND ALEATORY UNCERTAINTY

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

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