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Human experts can make judgments and
decisions based on uncertain, and often even conflicting, information. A
knowledge-based system that is required to perform at least at a similar level
of expertise, should be able to cope with this type of
information. For this reason, formalisms for representing uncertainty and
algorithms for manipulating uncertain information are important research
subjects within the field of Artificial Intelligence. Probability theory is one
of the oldest theories dealing with the concept of uncertainty; it is therefore
no surprise that the applicability of this mathematical theory as a model for
reasoning under uncertainty plays an important role. In this course, we will
consider probabilistic models for manipulating uncertain information in
knowledge-based systems. More specifically, we will consider the theory underlying the framework of probabilistic networks, and discuss the
construction of such networks for real-life applications.
http://www.cs.uu.nl/education/vak.php?vak=INFOPROB&jaar=2008
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