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Bayesian Modelling

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 July/August 2023 or February 2024.

Course Dates and Times

Monday 31 July – Friday 4 August 2023
Minimum 2 hours of live teaching per day
09:30 – 12:30 CEST

Chendi Wang

Vrije Universiteit Amsterdam

This course offers an immersive online learning environment that employs state-of-the-art pedagogical tools. With a maximum of 16 participants, our teaching team can provide personalised attention to each individual, catering to their specific needs. The course is designed for a demanding audience, including researchers, professional analysts, and advanced students.

Purpose of the course

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.

ECTS Credits

4 credits - Engage fully in class activities and complete a post-class assignment

Instructor Bio

Chendi is an assistant professor in political science at the Department of Political Science and Public Administration, VU Amsterdam. He holds a PhD in political science from the EUI.

Chendi's research interests lie in political behaviour, political economy, comparative politics, and quantitative and computational methods.

He has published in the British Journal of Political Science, Western European Politics, Comparative European Politics, and in volumes published by Cambridge University Press.

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

Day 1

Why Bayesian? Bayesian inference concepts, simulation-based inference and MCMC.

Day 2

The linear model, and models for binary outcome.

Day 3

Discrete choice outcomes and count outcomes.

Day 4

Hierarchical models and measurement models.

Day 5

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

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.