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Bayesian Networks

Course ID
NWI-IMC012
Credits
6 ec
Instructors
Johannes Textor, Perry Groot, Marcos L de Paula Bueno

Bayesian networks are powerful, yet intuitive tools for knowledge representation, reasoning under uncertainty, inference, prediction, and classification. The history of Bayesian Networks dates back to the groundbreaking work of Judea Pearl and others in the late 1980s, for which Pearl was given the Turing Award in 2012. Since then, Bayesian networks have evolved to become key parts of the data scientist's toolbox and are used in many application domains, notably medicine and molecular biology. This course will cover the necessary theory to understand, build, and work with Bayesian networks. Practical work will focus on implementing Bayesian networks in real-world domains.

Literature

There is no compulsory book for this course, the lecture notes will be self-contained. However, it is of course recommended to consult other sources during self-study, such as the books listed below. The first two books are available as e-books through the university library.

  • R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J.Spiegelhalter, Probabilistic Networks and Expert Systems, Springer, New York, 1999.

(Available as e-book via http://link.springer.com.ru.idm.oclc.org/book/10.1007%2Fb97670)

  • F.V. Jensen and T. Nielsen, Bayesian Networks and Decision Graphs, Springer, New York, 2007

(Available as e-book via http://link.springer.com/book/10.1007%2F978-0-387-68282-2)

  • K.B. Korb and A.E. Nicholson, Bayesian Artificial Intelligence, Chapman & Hall, Boca Raton, 2004 or 2010
  • C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
  • J. Pearl, Causality (2nd edition), Cambridge University Press, 2009