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BBNs are also sometimes called causal probabilistic networks, probabilistic cause-effect models or probabilistic influence diagrams. [Pg.214]

Chang K.C., Liu X, 1996. Efficient algorithms for learning probabilistic networks . Proceedings of the International Conference on Systems, Man and Cybernetics, IEEE2006, Volume 1. [Pg.228]

A. Frank and P. Pevzner. PepNovo De novo Peptide Sequencing via Probabilistic Network Modeling. Anal. Chem., 77, no. 4 (2005) 964-973. [Pg.223]

Cowell R. G., Dawid A. P., Lauritzen S. L., SpiegeUialter D. J. 1999. Probabilistic Networks and Expert Systems. New York Springer. [Pg.396]

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Friedman N., Murphy K. and Russell S. (1998). Learning the structure of dynamic probabilistic networks. Proceedings of the 14th Conference on the Uncertainty in Artificial Intelligence, pp 139-147. [Pg.397]

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An important consideration in many learning scenarios is the reliability of data and/or missing values. One learning method that is designed to reason in cases of uncertainty is that of Bayesian probabilistic networks. This method has been used to learn and model reaction mechanisms from physicochemical descriptions of instances of preclassified chemical reactions. [Pg.1522]

Mahboob Straub (2011). This may be a serious limitation for civil engineering applications. In principle event trees can deal with such dependencies however this requires great care during an analysis as observed by Faber Stewart (2003). Moreover both methods suffer from the difficulty in updating based on new information. Petri Nets provide a powerful platform, but the evaluation often takes basis in Monte Carlo simulations requiring considerable computational demands, Nishijima et al. (2009). These drawbacks can be overcome by the use of Bayesian probabilistic networks with discrete nodes supplemented by decision and utility nodes, Nielsen Jensen (2007). [Pg.2237]

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