Saint-Petersburg, Russia

Bayesian Statistics

when 30 July 2018 - 3 August 2018
language English
duration 1 week
credits 2 EC
fee USD 300

Bayesian data analysis is a rapidly developing field of statistics, which
has many useful applications in various areas of political science,
sociology, and international relations. The goal of this course is to
provide a brief and “mostly harmless” (that is, as informal as possible)
introduction to the theory and application of Bayesian statistical
methods. The course begins with the basic concepts of Bayesian
statistics (e.g., Bayes’s rule. priors, likelihood, and posterior
distribution). Then we consider various approaches to the estimation
and assessment of Bayesian models (with most attention to the
MCMC-based methods) in the context of generalized linear models.
Finally, we discuss applications of the Bayesian approach to specific
tasks arising in political research, including such topics as
multilevel/hierarchical analysis, Bayesian model averaging and the use
of informative priors in small-N comparative research.
Students are assumed to have basic knowledge of statistics and be
familiar with several conventional statistical methods, most
importantly regression analysis. Knowledge of advanced topics, such
as multilevel regression analysis and maximum-likelihood estimation,
is helpful, but not critical. In addition, for practical exercises we will
use R programming environment, so a basic knowledge of R is
desirable. However, it is not a necessary prerequisite, and a short
introduction to R will be given in the beginning of the course.

Course leader

Boris Sokolov, Higher School of Economics, Candidate of Science in
Political Science.
Boris Sokolov is a research fellow at the Laboratory for Comparative
Social Research and lecturer in the Department of Sociology, St.
Petersburg School of Social Sci

Target group

Master and PhD students in international relations, political science,
and/or sociology.

Course aim

Understanding the basic principles of Bayesian analysis, the
opportunities which this statistical method offers for social
scientists, and its limitations.
Being able to apply Bayesian model estimation techniques to
the analysis of real data sets.
Being able to properly report the results of Bayesian analysis in
research papers

Fee info

USD 300: The course fee is USD 300 or RUR 18000
RUB 18000: The course fee is USD 300 or RUR 18000


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