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

C. Baudrit (1) , S. Destercke (2) , Pierre-Henri Wuillemin (3)

1 I2M - Institut de Mécanique et d'Ingénierie de Bordeaux
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
3 DECISION

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.

Mots clés: imprecise probability; Dynamic credal networks; knowledge integration; modelling 24; uncertainty; Dirichlet 23 model

Domaines: Informatique [cs]

C. Baudrit, S. Destercke, Pierre-Henri Wuillemin. Unifying parameter learning and modelling complex systems with epistemic uncertainty using probability interval. Information Sciences, 2016, 367-368, pp.630-647. ⟨10.1016/j.ins.2016.07.003⟩. ⟨hal-01346202⟩