Stochastic modeling (CS)

Type: Normative

Department: radioelectronic and computer systems




SemesterAmount of hoursLecturerGroup(s)
1032Senior Researcher Sokolovskyi B. S.ФеІм-14, ФеІм-11, ФеІм-12, ФеІм-13

Laboratory works

SemesterAmount of hoursGroupTeacher(s)
1032ФеІм-14Senior Researcher Sokolovskyi B. S.

Опис навчальної дисципліни

The course covers theoretical, algorithmic, and applied aspects of stochastic modeling. Approaches to the development of stochastic models for deterministic and probabilistic systems and processes are considered. Methods for evaluating the statistical characteristics of the results of stochastic experiments are analyzed. Methods and algorithms for generating pseudorandom numbers, and modeling discrete and continuous random variables with different distribution laws are described. The methods of modeling random processes are considered. Considerable attention is paid to the use of probabilistic approaches to solving applied problems, including optimization.
The discipline aims to form students’ theoretical knowledge in the field of modern methods of stochastic modeling; and to acquire practical skills in the application of stochastic modeling methods to solve applied problems, including using specialized software products.


Recommended Literature

  • Taylor Howard M., Karlin Samuel. An Intoduction to Stochastic Modelling. – San Diego: Academic Press, 1998. – 631p.
  • Knuth Donald E. The Art of Computer Programming. Vol.2 .– Adisson Wesley Longman, 1998. – 763p.
  • Reuven V. Rubinstein Simulation and the Monte Carlo Method. –New York, Wiley & Sons, 1981. – 278p.
  • Sobol I. M. A Primer for the Monte Carlo Method. – London, CRC Press, 1994. –107p.


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