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Introduction to Bayesian Inference

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.

Course Dates and Times

Monday 6 – Friday 10 February 2023
Minimum 2 hours of live teaching per day
09:30 – 12:00 CET

Susumu Shikano

susumu.shikano@uni-konstanz.de

Institution: Universität Konstanz

This course provides a highly interactive blended learning environment, using state-of-the-art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 20 participants so that the teaching team can cater to the specific needs of each individual.

Purpose of the course

Political scientists increasingly apply the Bayesian approach to diverse kinds of research topics. This course is designed to equip you with the knowledge and skills needed to use the approach effectively and efficiently.

By the end of this course, you will:

gain a conceptual background for Bayesian analysis
understand the literature using Bayesian statistics, and how to interpret the results
learn how to use specific tools for Bayesian analysis, using examples from political science.

Instructor Bio

Susumu Shikano is Professor of Political Methodology at the University of Konstanz. His research interests are spatial models of politics and various topics in political behaviour.

His work has appeared in journals including Public Choice, Political Psychology, Party Politics, West European Politics, and the British Journal of Political Science.

@SusumuShikano

Political scientists are increasingly applying a Bayesian approach to diverse kinds of research topics. This is because it has many attractive features, including the ability to:

handle aggregate data without sampling processes
analyse small-N data
estimate models with complex likelihood functions.

The increasing capacity of modern computers is now enabling a wider range of researchers to conduct such computationally intensive estimations.

Despite these advantages, we must still address several challenges:

Bayesian statistics and conventional statistics are based on different views concerning theory and data, and this is not sufficiently acknowledged
the literature, including textbooks, is in general too technical to motivate most political scientists to apply Bayesian analysis to their research questions
programs needed for Bayesian analysis are not user-friendly enough for most political scientists.

The course aims to close these gaps, by:

providing a conceptual background for Bayesian analysis
showing you how to read the literature using Bayesian statistics, and interpret the results
introducing you to software for Bayesian analysis, with political science examples.

The course covers:

fundamentals of Bayesian analysis
estimation of linear regression models using conjugacy and Markov Chain Monte Carlo (MCMC)
estimation of various regression models (binary logit/probit, poisson, robust regression, etc) and further topics (model selection and check, data augmentation, etc) in Bayesian framework.

This is an introductory, one-week course. We cannot, therefore, cover the wide range of statistical models and advanced topics in Bayesian statistics. If you already have basic knowledge of Bayesian statistics, and if you can conduct regression analysis using JAGS/Stan, this course is not adequate.

How the course will work online

The course consists of lectures and lab sessions. The lecture deals with relevant background knowledge as well as specific skills for Bayesian analysis. In lab sessions, we'll apply these skills to political and social science data.

You will also gain basic knowledge of JAGS and Stan, enabling you to conduct Bayesian estimation using MCMC.

Knowledge in statistical analysis, including regression models with different types of dependent variables, is essential. During lab sessions, you will learn how to use JAGS and Stan from R. Therefore, the basic knowledge in R is also required.