Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Advanced inference in Bayesian networks
Learning in graphical models
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Causal Probabilistic Networks with Both Discrete and Continuous Variables
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exact Inference in Networks with Discrete Children of Continuous Parents
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Inference and Learning in Hybrid Bayesian Networks
Inference and Learning in Hybrid Bayesian Networks
Design and Analysis of Experiments
Design and Analysis of Experiments
A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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When modeling technical processes, the training data regularly come from test plans, to reduce the number of experiments and to save time and costs. On the other hand, this leads to unobserved combinations of the input variables. In this article it is shown, that these unobserved configurations might lead to un-trainable parameters. Afterwards a possible design criterion is introduced, which avoids this drawback. Our approach is tested to model a welding process. The results show, that hybrid Bayesian networks are able to deal with yet unobserved in- and output data.