Czech

Not applicable.

Fundamental concepts, methods and analytical techniques related to risk modelling will be studied. Specific ways of reasoning, typical for risk analysis and related model building will be developed and enhanced.

Basic knowledge of undergraduate mathematics (linear algebra, differential and integral calculus, probability and statistics, numerical methods), and computer technology for application software use.

Not applicable.

Teaching is carried out through lectures and seminars. Lectures consist of interpretations of basic principles, methodology of given discipline, problems and their exemplary solutions. Seminars particularly support practical mastery of subject matter presented in lectures or assigned for individual study with the active participation of students.

Course-unit credit requirements: active participation in seminars, mastering the subject matter, and delivery of semester assignment. Examination (written form): a practical part (4 tasks), a theoretical part (4 tasks); ECTS evaluation used.

1. Fundamental mathematical concepts of risk engineering.
2. Selected deterministic models for economic and financial computations.
3. Selected deterministic models for numerical and engineering approaches, sensitivity analysis.
4. Uncertainty in problems of risk engineering - stochastic and fuzzy models.
5. Problems of systems reliability and risks evaluations modelling, simulation approach.
6. Elementary models of decision making under uncertainty.
7. Selected estimation methods of models parameters probability distributions. Statistical software.
8. Advanced mathematical statistics methods - linear and nonlinear multiple regression analysis.
9. Elements of categorical, factor and cluster analysis.
10. Parametric and nonparametric statistical hypotheses tests.
11. Models for dynamic problems - introduction to Markov chains (applications in production systems).
12. Elements of time series analysis.
13. Basic models for quality control of production and produces.

Not applicable.

Students will learn useful knowledge of mathematical models focusing on risk modelling. They will also learn how apply studied models and methods within the framework of engineering processes.

Attendance at seminars is controlled and the teacher decides on the compensation for absences.

Not applicable.

Not applicable.

ANDĚL, J.: Základy matematické statistiky. Praha : Matfyzpress, 2005.
CIPRA, T.: Finanční matematika, MatFyzPress, 1993, ISBN 80-901495-1-0
CIPRA, T: Modely časových řad, SNTL, 1998.
HNILICA, J., FOTR, J.: Aplikovaná analýza rizika ve finančním managementu a investičním rozhodování. Praha : Grada, 2009, ISBN 978-80-247-2560-4.
KLAPKA A KOL.: Metody operačního výzkumu, VUTIUM 2001, ISBN 80-214-1839-7

KARPÍŠEK, Z.: Statistika a pravděpodobnost, CERM 2003, ISBN 80-214-2522-9
MONTGOMERY, D. C., RENGER, G.: Applied Statistics and Probability for Engineers. New York : John Wiley & Sons, 2003.
WILLIAMS, H. P.: Model Building in Mathematical Programming, Wiley 1993, ISBN 0471941115.