Probabilistic Models of Uncertainty in AI
- Department of Cybernetics
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.
- 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.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: