Abstract
Graphical modelling is a powerful framework for reasoning under uncertainty. We give an overview on the semantical background and relevant properties of probabilistic and possibilistic networks, respectively, and consider knowledge representation and independence as well as evidence propagation and learning such networks from data.
Whereas Bayesian networks and Markov networks are well-known for a couple of years, we also outline the perspectives of possibilistic networks as a tool for the efficient information-compressed treatment of uncertain and imprecise knowledge.
This work has partially been funded by CEC-ESPRIT III Basic Research Project 6156 (DRUMS II)
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Gebhardt, J., Kruse, R. (1995). Reasoning and learning in probabilistic and possibilistic networks: An overview. In: Lavrac, N., Wrobel, S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science, vol 912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59286-5_45
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