Evidential network-based extension of Leaky Noisy-OR structure for supporting risks analyses

Geoffrey Fallet (1, 2) , Philippe Weber (2) , Christophe Simon (2) , Benoît Iung (2) , Carole Duval (1)

1 EDF - EDF
2 CRAN - Centre de Recherche en Automatique de Nancy

Bayesian Networks (BN) are used in risks analysis because their capacities allow supporting complex system modeling. Nevertheless, to achieve some modeling, one BN issue is still the effort required for quantification even if some solutions are addressing the use of logical structures like OR, AND, Noisy-OR, Leaky Noisy-OR, etc. These structures are useful to represent different uncertainties but they do not allow taking into account uncertainty on their parameters, logically present in risks analysis. To face this challenge, this paper aims at proposing imprecise extensions of the Leaky Noisy-OR structures and a solution to implement these imprecise structures by using Evidential networks.

Mots clés: Bayesian Networks; Uncertainty; Noisy-OR; Leaky Noisy-OR; Evidential Networks

Domaines: Probabilités [math.PR]; Autre

Geoffrey Fallet, Philippe Weber, Christophe Simon, Benoît Iung, Carole Duval. Evidential network-based extension of Leaky Noisy-OR structure for supporting risks analyses. 8th International Symposium SAFEPROCESS 2012, Aug 2012, Mexico. pp.00. ⟨hal-00720902⟩