4 August 2023
Bayesian Modellingonline course
Gain an understanding of various Bayesian methods for answering research questions in quantitative social science, and learn how to apply them.
Need to know
This course requires basic knowledge in statistical analysis, including linear regression models and hypothesis testing. Some exposure to models with limited dependent variables (e.g., binary) is also required.
If you do not have this knowledge, take Introduction to Inferential Statistics or Applied Regression Analysis.
This course will use JAGS (or BUGS) and Stan through R. Therefore it is prefered that you to have basic knowledge of R, though it is not absolutely necessary. If you are completely new to R, consider taking Introduction to R.
Knowledge of maximum likelihood estimation (MLE) is an asset but not a prerequisite.
To maximise your learning, ensure you complete the required readings thoroughly each day and skim through at least one of the recommended readings, if available.
Key topics covered
Why Bayesian? Bayesian inference concepts, simulation-based inference and MCMC.
The linear model, and models for binary outcome.
Discrete choice outcomes and count outcomes.
Hierarchical models and measurement models.
Model assessment and comparison.
How the course will work online
The course is structured into five live Zoom sessions, each lasting 2.5 to 3 hours. During these sessions, you will focus on two main tasks: understanding the theoretical concepts behind different models and hands-on coding exercises embedded in the lecture. Through the hands-on coding exercises, you will learn how to master the technical aspects of Bayesian modelling in R, and apply these methods to real-world data.
Prior to each session, the Instructor will distribute the slides and R script for you to explore at your own pace. During the session, the instructor will go through the code and models with you. Additionally, you will take time to get to know each other and discuss how Bayesian modelling can help answer your research questions.
After each session, there will be problem sets for you to complete, which will be discussed together the following day. If you have any questions or thoughts to share, you can post them on Canvas, and the Instructor will host live Q&A sessions. You can also sign up for a quick one-to-one consultation during designated office hours.
Chendi Wang is assistant professor in political science at VU Amsterdam.
His research interests include political behaviour, political economy, comparative politics, and quantitative and computational methods.
Researchers, professional analysts, and advanced students.
Throughout the course, you will learn how to apply various Bayesian methods to answer research questions in quantitative social science. In addition to the theoretical material, you will gain proficiency in data analytics using the open-source statistical programming language R.
By the end of the course, you will:
• understand the fundamental differences and similarities between frequentist and Bayesian approaches to inference;
• formulate linear and generalised linear models, hierarchical models, and measurement models in the Bayesian framework using JAGS or Stan;
• interpret models in the Bayesian framework; and
• compare and assess Bayesian models and apply Bayesian methods to political science research questions.
Overall, the course will equip you with advanced knowledge and skills that will be useful in your research, analysis, and decision-making. If you're a researcher, professional analyst, or advanced student seeking to enhance your quantitative methods expertise, this course is ideal for you.
You can earn up to four credits for attending this course.
3 ECTS credits – Attend 100% of live sessions and engage fully with class activities.
4 ECTS credits – Attend 100% of live sessions, engage fully with class activities and complete a post-class assignment.
GBP 478: ECPR Member
GBP 956: ECPR Non-Member
Funding applications for the 2023 ECPR Summer School in Research Methods and Techniques are now closed.
For more details on funding opportunities for ECPR's other events, please visit our website.