Subject description - XP33PMD
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Explanatory Notes
Instructions
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:
Page updated 1.3.2021 17:52:22, 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) |