Unifying parameter learning and modelling complex systems with epistemic uncertainty using probability interval
Language
en
Article de revue
This item was published in
Information Sciences. 2016-11, vol. 367-368, p. 630–647
Elsevier
English 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 ...Read more >
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.Read less <
English Keywords
modelling 24
knowledge integration
uncertainty
Dynamic credal networks
imprecise probability
Dirichlet 23 model
Origin
Hal imported