Subject description - XP33PMD

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XP33PMD Probabilistic Models of Uncertainty in AI
Roles:S Extent of teaching:2P+0S
Department:13133 Language of teaching:CS
Guarantors:Jiroušek R., Pošík P. Completion:ZK
Lecturers:Jiroušek R., Pošík P. Credits:4
Tutors:Jiroušek R., Pošík P. Semester:L

Anotation:

Basic (discrete) probability. Foundations of graph theory. Triangulated graphs and their characteristics. Information as a measure of dependence. Conditional independence (Factorization Lemma, Block Independence Lemma). Knowledge representation by multidimensional distributions. Qualitative knowledge represented by dependence structures. Graphical Markov models and Bayesain networks. Decomposable models for computation in Graphical Markov models. Examples of application.

Course outlines:

Exercises outline:

Literature:

F. V. Jensen, Bayesian Networks and Decision Graphs. Springer Verlag, New York 2001.
Jiroušek, R., Scozzafava, R.: Basic Probability. Lecture notes for PhD. studies 1/2003. Faculty of Management, Jindřichův Hradec, University of Economics, Prague, 2003.
S. L. Lauritzen: Graphical Models. Clarendon Press, Oxford 1996.

Requirements:

Subject is included into these academic programs:

Program Branch Role Recommended semester
DOKP Common courses S
DOKK Common courses S


Page updated 5.3.2021 17:52:09, semester: Z/2020-1, L/2021-2, L/2020-1, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)