Course detail

Stochastic Modelling

FSI-S2MAcad. year: 2019/2020

The following topics are dealt with: characteristic functions of random variables and vectors, functions of random vector and their statistical analyses, multiple normal distribution, fitting of probability distributions by means of classical statistical methods, kernel estimates and quasinorms.

Learning outcomes of the course unit

Students acquire needed knowledge from important parts of the probability theory and mathematical statistics, which will enable them to use PC model and optimize responsible characteristics and properties of technical systems and processes.


Methods of mathematical analysis of real and complex functions, probability theory and mathematical statistics.


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Anděl, J.: Statistické metody. Praha : Matfyzpress, 1993. (CS)
Gallant, A. R.: Nonlinear Statistical Models. New York : John Wiley, 2003. (EN)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. London : Chapman & Hall, 1999. (EN)
Potocký, R. a kol.: Zbierka úloh z pravdepodobnosti a matematickej štatistiky. Bratislava/Praha : Alfa/SNTL, 1986. (SK)
Likeš, J. - Machek, J.: Matematická statistika. Praha : SNTL, 1983. (CS)
Pitman, E. J. G.: Some Basic Theory for Statistical Inference. New York :John Wiley & Sons, 1978. (EN)
MONTGOMERY, Douglas C. a George C RUNGER. Applied statistics and probability for engineers. 5th ed. Hoboken: John Wiley, 2011, xv, 768 s. : il. ; 27 cm. ISBN 978-0-470-05304-1. (EN)

Planned learning activities and teaching methods

The course is taught through exercises which are focused on practical topics presented in lectures.

Assesment methods and criteria linked to learning outcomes

Graded course-unit credit requirements: active participation in seminars, mastering the subject matter, assignments elaboration; evaluation is based on the semester assignment results.

Language of instruction


Work placements

Not applicable.


The course objective is to make students familiar with selected parts from probability theory and mathematical statistics, which extend students` knowledge acquired in previous courses. In addition other methods for modelling technical processes on PC are introduced.

Specification of controlled education, way of implementation and compensation for absences

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

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MAI , 1. year of study, winter semester, 3 credits, elective (voluntary)

Type of course unit



26 hours, compulsory

Teacher / Lecturer


Characteristic function of random variable, properties.
Calculating characteristic function of random variables.
Moments of random variables by the help of characteristic function.
Characteristic function of random vector, properties.
Function of random variable and random vector, convolution.
Estimates for function of random variable and random vector.
Multiple normal probability distribution, properties.
Gram - Charlier models A and B.
Pearson curves, Edgeworth and Johnson model.
Kernel estimates of probability density.
Entropy of probability distribution.
Estimates of distribution by the help of minimum Shannon quasinorm.
Estimates of distribution by the help of minimum Hellinger quasinorm.