Бакалавриат
2026/2027
Стохастические процессы и приложения
ID 1139683
Статус:
Курс обязательный (Прикладной анализ данных)
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 1, 2 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
4
Контактные часы:
56
Course Syllabus
Abstract
This course is conducted at Data Science and Business Analytics program and is provided to 3rd-year undergraduates who have studied a course covering basic probability and statistical inference. A half of this course introduces concepts of Markov chains, random walks, martingales. The course requires basic knowledge in probability theory and linear algebra. It introduces students to the modeling, quantification and analysis of uncertainty. The main objective of this course is to developthe skills needed to do empirical research in fields operating with a concept of Stochastic processes and its applications. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data.
Learning Objectives
- The main objective of this course is to develop the skills needed to do empirical research in fields of stochastic processes and application in finance
Expected Learning Outcomes
- Students get an understanding of techniques and receipts for estimation and assessment of the quality of economic models with time-series data.
Course Contents
- Discrete-time martingale theory.
- Continuous-time stochastic processes.
- Stochastic calculus and differential equations.
- Continuous-time financial models.
Bibliography
Recommended Core Bibliography
- Applied econometric time series, Enders, W., 2015
- Stochastic processes : theory for applications, Gallager, R. G., 2015
- The econometric modelling of financial time series, Mills, T. C., 2004
- Time series models, Harvey, A. C., 1993
Recommended Additional Bibliography
- An introduction to mathematical statistics and its applications, Larsen, R. J., 2012
- Mathematical statistics with applications, Wackerly, D. D., 2008
- Probability and statistical inference, Hogg, R. V., 2015