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Journal Articles Information Sciences Year : 2016

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

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Abstract

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.
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Dates and versions

hal-01346202 , version 1 (18-07-2016)

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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⟩
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