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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.	Completion:	ZK
Lecturers:	Jiroušek R.	Credits:	4
Tutors:	Jiroušek R.	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	–

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Page updated 23.1.2021 10:29:48, 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)