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For more information about the lecture halls and seminar rooms, see our lecture room page. The names listed under "Technical Assistance" are student volunteers who will act as a contact person for technical questions of the lecturers and workshop speakers during the course or workshop.

Probabilistic logics and probabilistic networks

To reason with uncertainties, we can draw on probability theory and on deductive logic. But these are very different formalisms. Moreover, probabilistic logics can be hard to understand, and inference with probabilities easily become computationally intractable. In our course we introduce a framework that can accommodate both logic and probability theory, and that covers a large number of the available probabilistic logics: Bayesian inference, evidential probability, Dempster-Shafer theory, statistical inference, inductive logic, and objective Bayesianism. We further introduce graphical representations of the probability assignments known as credal networks. We show that these networks provide an intuitive grasp of the inferences, and keep the inferences computationally tractable. We end the course with a review of some applications of the framework in AI, specifically in data mining.