Probability Theory and Mathematical Statistics (Informatics)

Type: Normative

Department: discrete analysis and intelligent system




SemesterAmount of hoursLecturerGroup(s)
332Professor M. M. PrytulaPMi-21, PMi-22, PMi-23, PMi-24, PMi-25, PMo-21
432Professor M. M. PrytulaPMi-21, PMi-22, PMi-23, PMi-24, PMo-21

Laboratory works

SemesterAmount of hoursGroupTeacher(s)
332PMi-21H. A. Kvasnytsia, O. Ya. Priadko
PMi-22O. Ya. Priadko, M. O. Starchak
PMi-23H. A. Kvasnytsia, O. Ya. Priadko
432PMi-21H. A. Kvasnytsia, O. Ya. Priadko
PMi-22H. A. Kvasnytsia, O. Ya. Priadko
PMi-23H. A. Kvasnytsia, O. Ya. Priadko

Course description

Aim. Study the probabilistic-statistical methods that will further allow studying of more specialized subjects, based on probabilistic models.

Summary. The course covers the following topics: the probability of random events; a sequence of independent trials; random variables; numerical characteristics of random variables; the law of large numbers; characteristic functions of random variables; Markov chain; stochastic processes. The core concepts of mathematical statistics; an estimation of unknown parameters of the distributions; criteria based on a comparison of probabilities and corresponding frequencies; variational analysis; correlation and regression analysis.

Target. Master the basic theoretical aspects of probabilistic modeling in technical, economic and ecological systems. Master the techniques of solving probabilistic problems in practice, using modern computer technology and problem-oriented software packages.

After completion of this course a student should

  • know: the main aspects of selection and construction of probabilistic models that are used to solve technical, economic and management problems;
  • be able to: apply probabilistic models in practice for mathematical modeling of processes in technical systems; solve optimization problems in technical process management using probabilistic methods.