Module 1. Probabilistic bases of the simulation method
Course: System Analysis
Structural unit: Faculty of Computer Science and Cybernetics
Title
Module 1. Probabilistic bases of the simulation method
Code
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
8 Semester
Number of ECTS credits allocated
2
Learning outcomes
Know and understand the main types of simulation models, principles of simulation modeling, definition and characteristics of basic random variables, examples of basic sensors, basic methods of simulation of random variables with discrete and absolutely continuous distributions, principles of modeling random vectors and processes, including queuing systems; methods of verification of simulation results.
Be able to model the values of the basic random variable, to verify the obtained simulation results, to model samples of given volumes for random variables with discrete and absolutely continuous distributions, to model the values of random vectors and processes; apply simulation models for forecasting and consistency with real statistics.
Demonstrate the ability to self-study and continue professional development.
Demonstrate skills of interaction with other people, ability to work in teams.
Form of study
Full-time form
Prerequisites and co-requisites
Know: basics of probability theory, mathematical statistics, programming
Be able to: formalize tasks and compile algorithms for the implementation of tasks
Have basic skills: program in one of the modern languages
Course content
The discipline "Probabilistic bases of the simulation method" is an integral part cycle of professional training of specialists of educational and qualification level "bachelor" and considers simulation modeling of stochastic objects from the simplest (basic random value) to complex (queuing systems). Considered as theoretical principles such modeling, and various algorithms for implementing these tasks in the form of software code. Discipline is the discipline of choice. Uses the concept of "probability theory",
"Mathematical analysis", "discrete mathematics", "programming", "random theory" processes "and" decision-making methods ". Taught in the 8th semester, volume 66 hours. (2 credits ECTS), of which lectures - 16 hours, independent work - 50 hours. There are 2 content parts.
Recommended or required reading and other learning resources/tools
1. Yu.S. Harin, M.D. Stepanova "Computer Workshop on Mathematical Statistics", Minsk, "University", 1987.
2. В.В. Anisimov, OK Zakusilo, VS Donchenko "Elements of queuing theory and asymptotic analysis of systems", Kyiv, 1987.
3. S.M. Ермаков, Г.А. Mikhailov "Statistical Modeling", Moscow, "Science", 1982.
4. AS Shalygin, Yu.I. Palagin "Applied methods of statistical modeling", Leningrad, 1986.
5. A.K. Kutz et al. “Social systems. Formalization and computer modeling ", Omsk, 2000
6. A.K. Kutz et al. “Computer modeling. Tools for the study of social systems ", Omsk, 2001
7. D.A. Иванников, С.М. Кашаев, Л.В. Sherstneva "Modeling of random processes", Methodical instructions for laboratory work №2 in the discipline
"Modeling of information processes and systems", Nizhny Novgorod 2001.
Planned learning activities and teaching methods
Lecture, individual work
Assessment methods and criteria
Test work, exam.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline