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Course detail

Stochastic Modelling

Course unit code: FSI-S2M
Academic year: 2016/2017
Type of course unit: optional (voluntary)
Level of course unit: Master's (2nd cycle)
Year of study: 1
Semester: winter
Number of ECTS credits:
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.
Mode of delivery:
90 % face-to-face, 10 % distance learning
Methods of mathematical analysis of real and complex functions, probability theory and mathematical statistics.
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
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.
Recommended or required reading:
Anděl, J.: Statistické metody. Praha : Matfyzpress, 1993.
Gallant, A. R.: Nonlinear Statistical Models. New York : John Wiley, 2003.
Potocký, R. a kol.: Zbierka úloh z pravdepodobnosti a matematickej štatistiky. Bratislava/Praha : Alfa/SNTL, 1986.
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. London : Chapman & Hall, 1999.
Likeš, J. - Machek, J.: Matematická statistika. Praha : SNTL, 1983.
Pitman, E. J. G.: Some Basic Theory for Statistical Inference. New York :John Wiley & Sons, 1978.
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.
Course curriculum:
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.

Type of course unit:

seminars: 26 hours, compulsory
Teacher / Lecturer: doc. RNDr. Zdeněk Karpíšek, CSc.
Syllabus: 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.

The study programmes with the given course