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.