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Markov Distributions on Graphs - NSTP127

Title:	Markovské distribuce nad grafy
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2014 to 2017
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	Ing. František Matúš, CSc.
Classification:	Mathematics > Probability and Statistics
Interchangeability :	NMTP574
Is incompatible with:	NMTP574
Is interchangeable with:	NMTP574

Annotation -

Last update: T_MUUK (31.01.2001)

Graphical models for categorial and gaussian data.

Aim of the course -

Last update: T_KPMS (13.05.2008)

Fundamentals of graphical models over undirected and directed graphs, for categorical and gaussian variables

Literature - Czech

Last update: G_M (27.05.2009)

S. L. Lauritzen (1996) Graphical Models. Clarendon Press, Oxford

J. Whittaker (1990) Graphical Models in Applied Multivariate Statistics. John Wiley and Sons, New York

Teaching methods -

Last update: G_M (27.05.2008)

Lecture.

Syllabus -

Last update: T_KPMS (16.05.2003)

1. Graphs, triangulated graphs, decopmositions.

2. Decomposable hypergraphs. Directed acyclic graphs, moral graphs.

3. Conditional independence, Markov properties.

4. Hammersley-Clifford theorem.

5. Contingency tables. Maximal likelihood estimation in graphical models.

6. Gaussian graphical models. Iterative methods for solving likelihood equations.

7. Probabilistic expert systems.