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Non-linear Modeling of a Production Process by Hybrid Bayesian Networks

Rainer Deventer, Joachim Denzler, Heinrich Niemann

This paper shows how non-linear functions can be approximated by hybrid Bayesian networks. The basic idea is to make a piecewise linear approximation with several base points. This approach is applied to an engineering domain and the accuracy is compared to Gibbs sampling. Great accuracy is shown even at non-continuous functions. Due to the general underlying principle,it is possible to adapt this type of network to other domains.

Keywords: Probabilistic Networks, Bayesian Learning

Citation: Rainer Deventer, Joachim Denzler, Heinrich Niemann: Non-linear Modeling of a Production Process by Hybrid Bayesian Networks. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.576-580.


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ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.