Abstract
The field of Bayesian networks, and graphical models in general, has grown enormously over the last few years, with theoretical and computational developments in many areas. As a consequence there is now a fairly large set of theoretical concepts and results for newcomers to the field to learn. This tutorial aims to give an overview of some of these topics, which hopefully will provide such newcomers a conceptual framework for following the more detailed and advanced work. It begins with revision of some of the basic axioms of probability theory.
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© 1998 Springer Science+Business Media Dordrecht
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Cowell, R. (1998). Introduction to Inference for Bayesian Networks. In: Jordan, M.I. (eds) Learning in Graphical Models. NATO ASI Series, vol 89. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5014-9_1
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DOI: https://doi.org/10.1007/978-94-011-5014-9_1
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