Bayesian updating in causal probabilistic networks by local computations

213 of Studies in fuzziness and soft computing, Springer, pp. Salmeron (eds), Advances in probabilistic graphical models, Vol. Cabañas, R., Cano, A., Gómez-Olmedo, M., and Madsen, A. Madsen, Bayesian Network Inference with Simple Propagation, Twenty-ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS), 2016, pages 650-655. Modeling concept drift: A probabilistic graphical model based approach. Parallel importance sampling in conditional linear Gaussian networks. The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA’15). L., Jensen, F., Salmerón, A., Langseth, H., Nielsen, T. In proceedings of ECSQARU on 15-17 July 2015 in Compiegne, France, pages 529-540. Early Recognition of Maneuvers in Highway Traffic .

MPE Inference in Conditional Linear Gaussian Networks. Proceedings of the 2015 IEEE International Conference on Industrial Informatics (INDIN) Cambridge, United Kingdom. A Methodology for Developing Local Smart Diagnostic Models Using Expert Knowledge. In proceedings of 2014 IEEE Multi-Conference on Systems and Control on 8-10 October 2014 in Nice, France, pages 1626-1631.

Optimizing Bayesian Networks for Recognition of Driving Maneuvers to Meet the Automotive Requirements. D., Hovda, S., Fernández, A., Langseth, H., Madsen, A.

dos Santos, On Tree Structures used by Simple Propagation for Bayesian Networks Inference, Twenty-ninth Canadian Conference on Artificial Intelligence (AI), 2016, pages 207-212.