Master
2022/2023
Bayesian Methods for Data Analysis
Type:
Compulsory course (Financial Technologies and Data Analysis)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
2 year, 1 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Финансовые технологии и анализ данных
Language:
English
ECTS credits:
5
Contact hours:
28
Course Syllabus
Abstract
This course introduces the basic theoretical and applied principles of Bayesian statistical analysis in a manner geared toward students in the social sciences. The Bayesian paradigm is particularly useful for the type of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed. The course consists of three main sections: a Bayesian approach to probability theory, sampling methods, and major types of generative models.