Matthieu Hourbracq, Cedric Baudrit, Pierre-Henri Wuillemin, Sebastien Destercke

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Dynamic Credal Networks: introduction and use in robustness analysis

Abstract

Dynamic Bayesian networks (DBN) are very handy tools to model complex dynamical system described by collected data and expert knowledge. However, expert knowledge may be incomplete, and data may be scarce (this is typically the case in Life Science processes). In such cases, using precise parameters to describe the network does not faithfully account for our lack of information. This is why we propose, in this paper, to extend the notion of Dynamic Bayesian networks to convex sets of probabilities, introducing the notion of dynamic credal networks (DCN). We propose different extension relying on different independence concepts, briefly discussing the difficulty of extending classical algorithms for each concepts. We then apply DCN to perform a robustness analysis of DBN in a real-case study concerning the microbial population growth during a French cheese ripening process.

Keywords

Dynamic Bayesian Network, Credal Network, Robustness analysis, Independence, Inference Algorithms, Food processing

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E-mail addresses

Matthieu Hourbracq		matthieu.hourbracq@lip6.fr
Cedric Baudrit		cbaudrit@grignon.inra.fr
Pierre-Henri Wuillemin		pierre-henri.wuillemin@lip6.fr
Sébastien Destercke		sebastien.destercke@utc.fr