Бакалавриат
2026/2027
Теория вероятностей и математическая статистика
ID 1135695
Статус:
Курс обязательный (Международная программа по бизнесу и экономике)
Кто читает:
Департамент математики
Где читается:
Школа информатики, физики и технологий
Когда читается:
2-й курс, 1, 2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
6
Контактные часы:
98
Course Syllabus
Abstract
The goal of studying the discipline is learning the methods of computation of probabilities of random events and probability distributions of random variables, solving statistical estimation problems, notions of the theory of statistical hypotheses testing, that allow the student to apply this knowledge in the disciplines such as “Methods of Optimal Solution”, “Mathematical Models in Economics”, “Game Theory”, “Econometrics”. The course “Probability Theory and Mathematical Statistics” will be used in the theory and applications of multidimensional statistical analysis, mathematical economics, econometrics. The material of the course can be used for development and application of numerical methods of solving problems in various regions sciences and for creating and studying mathematical models of such problems.
Learning Objectives
- The goal of studying the discipline is learning the methods of computation of probabilities of random events and probability distributions of random variables, solving statistical estimation problems, notions of the theory of statistical hypotheses testing, that allow the student to apply this knowledge in the disciplines such as “Methods of Optimal Solution”, “Mathematical Models in Economics”, “Game Theory”, “Econometrics”. The course “Probability Theory and Mathematical Statistics” will be used in the theory and applications of multidimensional statistical analysis, mathematical economics, econometrics. The material of the course can be used for development and application of numerical methods of solving problems in various regions sciences and for creating and studying mathematical models of such problems.
Expected Learning Outcomes
- the student can define the relevant sample space, compute the probabilities of random
- can solve problems about random variables and their characteristics
- can use Chebyshev inequality and Markov inequality
- can compute sample characteristics, construct the empirical distribution function, histogram and the frequency polygon
- can solve problems about construction of confidence intervals for the parameters of the normal sistribution, test the hypothesis about the mean for samples from the normal distribution
- can find the estimates of the parameters of the distribution
- can test parametric and nonparametric hypotheses
- can solve problems about two dimensional random variables and their charactersitics
Course Contents
- 1. Events and Bernoulli trials
- 2. One dimensional random variables
- 3. Law of Large Numbers and Central Limit Theorem
- 4. Two dimensional random variables.
- 5. Basic notions of mathematical statistics
- 6. Samples from normal distribution
- 7. Statistical hypotheses testing
- 8. Statistical theory of parameter estimation
Interim Assessment
- 2026/2027 2nd module0.08 * In-class activity + 0.27 * Test 1 + 0.27 * Test 2 + 0.38 * Exam
Bibliography
Recommended Core Bibliography
- Marcelo Sampaio de Alencar, & Raphael Tavares de Alencar. (2016). Probability Theory. Momentum Press.
Recommended Additional Bibliography
- Linde, W. (2017). Probability Theory : A First Course in Probability Theory and Statistics. [N.p.]: De Gruyter. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1438416