Course detail
Stochastic Modelling
FSI-S2M-A Acad. year: 2022/2023 Winter semester
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.
Language of instruction
English
Number of ECTS credits
3
Supervisor
Department
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.
Prerequisites
Methods of mathematical analysis of real and complex functions, probability theory and mathematical statistics.
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.
Aims
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.
The study programmes with the given course
Programme N-MAI-A: Mathematical Engineering, Master's, elective
Type of course unit
Exercise
26 hours, compulsory
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.