Probability theory and mathematical statistics

Course: System Analysis

Structural unit: Faculty of Computer Science and Cybernetics

Title
Probability theory and mathematical statistics
Code
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
11
Learning outcomes
Know and understand the basic definitions, formulas (including total probability and Bayesian formulas), lemmas, theorems, models, concepts (including the concept of event independence and conditional probability) and the position of the discipline (including axiomatics of probability theory), basic characteristics of random variables and their properties; main properties of Markov chain models with discrete and continuous time. Know and understand the basic formulas, models, concepts and problems of mathematical statistics. Be able to perform calculations within the finite and calculated probability schemes and in terms of the geometric probability model; build and investigate probability distributions of discrete, continuous, singular and mixed random variables; check the dependence and independence of events and random variables.
Form of study
Full-time form
Prerequisites and co-requisites
Know: basics of probability theory and mathematical statistics, mathematical analysis and algebra Be able to: apply knowledge of probability theory and mathematical statistics. Have basic skills: solve problems in probability theory. To access the discipline "Probability Theory and Mathematical Statistics" of the educational-professional program "Systems Analysis" the student must master the results of learning provided by the disciplines "Mathematical Analysis", "Algebra", "Discrete Mathematics". The discipline "Probability Theories and Mathematical Statistics" is the basis for mastering the disciplines "Mathematical Demography and Random Process Modeling", "Mathematical Insurance Models and Asymmetric Cryptography", "Customer Theory".
Course content
The discipline "Probability Theory and Mathematical Statistics" is part of the educational and professional training program for the first (bachelor's) level of higher education in the field of knowledge 12 "Information Technology" in 124 "System Analysis", educational and professional program "System Analysis". This course is a compulsory subject in the System Analysis program. It is taught in 4 and 5 semesters in the amount of 330 hours. (11 ECTS credits) in particular: lectures - 74 hours, practical - 74 hours, consultations - 8 hours, independent work - 174 hours. The course provides 4 content parts and 4 tests. The discipline ends with a test in the 4th semester and an exam in the 5th semester.
Recommended or required reading and other learning resources/tools
1. Victor Barkovsky, Nina Barkovskaya, Alexey Lopatin. Probability theory and mathematical statistics.- Center for Educational Literature. - 2019. - 424 p. 2. Vadym Radchenko, Oleksandr Borysenko, Yulia Mishura, Heorhiy Shevchenko. Collection of problems in financial mathematics. - Technology. - 2007. - 256p. 3. Gichman, A. Skorokhod, M. Yadrenko "Probability theory and mathematical statistics". 4.А.В. Skorokhod "Elements of probability theory and the theory of random processes", K. 1975. 5.A. Dorogovtsev “Probability theory. Collection of problems ", K. 1980. 6. Lebedev EA, Sharapov MM Course of lectures on probability theory. - K .: Norita-plus, 2007. - 168 p. 7. EO Lebedev, OA Chechelnytsky, MM Sharapov, MS Bratiychuk Collection of problems in probability theory, KNU. T. Shevchenko, 2006.
Planned learning activities and teaching methods
Lecture, practical classes, independent work
Assessment methods and criteria
Test work, exam
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Hanna Volodymyrivna Livinska
Applied Statistics
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Applied Statistics
Faculty of Computer Science and Cybernetics