Introduction to Bayesian Inference

Political scientists increasingly apply the Bayesian approach to diverse kinds of research topics. This development is due to a series of its attractive features: e.g. handling aggregate data without sampling processes, analysing small N data, estimating models with complex likelihood functions. Furthermore, the increasing capacity of modern computers enables a wider range of researchers to conduct such computationally intensive estimations.

Despite of these advantages there is still backlog demand in respect to several points: First, it is not enough acknowledged that Bayesian statistics and conventional statistics are based on different views concerning theory and data. Second, the literature, including text books, is in general too technical to motivate most political scientists to apply Bayesian analysis to their own research questions. Third, the programs needed for Bayesian analysis is not user friendly enough for most political scientists.

The course aims to close these gaps. First, the course provides a conceptual background for Bayesian analysis. Second, participants will be guided how to read the literature using Bayesian statistics and interpret the results. Third, this course introduces to a specific software for Bayesian analysis with political science examples.

More specifically, the course covers the following topics: 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, multi-level, robust regression etc.) in Bayesian framework. 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, these skills are applied to political and social science data. Hence, course participants also learn the basic knowledge of JAGS, which enables to conduct Bayesian estimation using MCMC.

Participants are required to have knowledge in statistical analysis including regression models with different types of dependent variables. Further, they are also required to have knowledge about maximum likelihood estimation (MLE). At least, participants have to know how to calculate the likelihood of certain parameter sets of a statistical model given observed data. In lab sessions participants learn how to use JAGS from R. Therefore, the basic knowledge in R is also required.

Note that this is an introductory one week course. This course, therefore, cannot thoroughly treat the wide range of statistical models and further advanced topics in Bayesian statistics. For those who have basic knowledge in Bayesian statistics and can conduct regression analysis using JAGS, this course is not adequate.

Prior knowledge of statistics including regression models with different types of dependent variables. Knowledge about maximum likelihood estimation (MLE), in particular the participants should be able to distinguish likelihood . Basic knowledge of R.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.