- Department of Applied Mathematics
An understanding of model development and identification of stochastic systems. The course is devoted to large network systems, with the goal to develop skill in the prediction and control of traffic flow in such networks. Stochastic transport models are based on statistical data description with Bayesian rules.
Basic technical university level of Probability and Mathematical Statistics; at least passive knowledege of differential and difference calculus. Elementary knowledge of programming in arbitrary language.
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
Basic knowledge in the field of stochastic processes - description of stochastic processes, their estimation and use for prediction. Ability to apply the theory on transportation problems.
- Study materials:
J. Anděl: Matematická statistika, SNTL, 1978, Prague., M. Loeve: Probability Theory, van Nostrand, 1962, Princeton, New Jersey., I. Nagy: Základy bayesovského odhadování a řízení, publisher CTU, 2003, Prague., V. Peterka: Bayesian approach to system identification, Oxford, P. Eykhoff ed., Pergamon Press., 1981, oEikohoff P.: System identification. Parameter and state estimation, John Wiley ?Sons, 1974
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: