BAUDRIT, Cedric; DESTERCKE, S.; WUILLEMIN, Pierre-Henri

doi:10.1016/j.ins.2016.07.003

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BAUDRIT, Cedric

DESTERCKE, S.
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]

WUILLEMIN, Pierre-Henri
DECISION

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Information Sciences. 2016-11, vol. 367-368, p. 630–647

Elsevier

Résumé en anglais

Modeling complex dynamical systems from heterogeneous pieces of knowledge varying in precision and reliability is a challenging task. We propose the combination of dynamical Bayesian networks and of imprecise probabilities to solve it. In order to limit the computational burden and to make interpretation easier, we also propose to encode pieces of (numerical) knowledge as probability intervals, which are then used in an imprecise Dirichlet model to update our knowledge. The idea is to obtain a model flexible enough so that it can easily cope with different uncertainties (i.e., stochastic and epistemic), integrate new pieces of knowledge as they arrive and be of limited computational complexity.< Réduire

Mots clés en anglais

modelling 24

knowledge integration

uncertainty

Dynamic credal networks

imprecise probability

Dirichlet 23 model

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Unifying parameter learning and modelling complex systems with epistemic uncertainty using probability interval

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