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Last update: T_KPMS (16.05.2013)
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Last update: RNDr. Milan Studený, DrSc. (24.05.2016)
To explain basic mathematical methods for dealing with probabilistic conditional independence structures. |
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Last update: RNDr. Jitka Zichová, Dr. (17.05.2022)
Oral exam. |
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Last update: RNDr. Milan Studený, DrSc. (24.05.2016)
S.L. Lauritzen: Graphical Models. Clarendon Press 1996.
M. Studený: Struktury podmíněné nezávislosti. MatfyzPress 2014. (skripta v češtině) |
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Last update: RNDr. Milan Studený, DrSc. (24.05.2016)
Lecture, possibly combined with consulted reading of the literature. |
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Last update: RNDr. Jitka Zichová, Dr. (02.03.2018)
Zkouška je ústní. Zkouší se pojmy a výsledky z cyklu přednášek, konkrétněji:
V rámci zkoušky se studentům zadají některá z cvičení k dané látce, jejich zadání bude dostupné na internetu. |
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Last update: RNDr. Milan Studený, DrSc. (24.05.2016)
The concept of conditional independence (CI). Basic formal properties of CI, the concept of a semi-graphoid and (formal) CI structure. Basic method of construction of measures inducing CI structures. Information-theoretical tools for CI structure study. Graphical methods for CI structure description: undirected graphs (= Markov networks), acyclic directed graphs (= Bayesian networks). The method of local computation.
Possible additional topics: The (non-existence of a) finite axiomatic characterization of CI structures. Learning graphical models from data. Chain graphs. |
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Last update: RNDr. Milan Studený, DrSc. (20.05.2019)
The students should be familiar with elementary concepts from measure theory and lattice theory, basic facts about matrices and with basic concepts from graph theory and convex geometry. The knowledge of basic statistical distributions is useful, although not necessary. All above mentioned concepts can be found in the appendix of the lecture notes. |