Probabilistic Models of Uncertainty in AI

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

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.

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

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.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Doctoral studies, daily studies (compulsory elective course)
• Doctoral studies, combined studies (compulsory elective course)
• Doctoral studies, structured daily studies (compulsory elective course)
• Doctoral studies, structured combined studies (compulsory elective course)