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Member rate £572.50
Non-Member rate £1,145.00
Early bird discount available until 14 December 2026.
Save £45.00 Loyalty discount applied automatically*
Save 5% on each additional course booked
* If you attended a qualifying previous Methods School in 2026, you qualify for £45.00 off your course fee.
Date: Monday – Friday, 15 – 19 February 2027
Time: Monday – Tuesday 09:30 – 12:30 CET; Wednesday – Friday 13:30 – 16:30 CET
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This interactive course uses advanced pedagogical tools and is designed for master's-level students and above. It runs as three-hour live sessions on Zoom over five days, with course materials and pre-course readings available on our Learning Management System at least two weeks before the start. Office hours are also available for one-to-one consultations on your own project.
The course is limited to 18 participants, ensuring that the teaching team can address the individual needs of each participant.
This course introduces you to Bayesian modelling for quantitative social science, with a particular focus on how Bayesian methods can help you answer substantive research questions. You will learn the logic of Bayesian inference and how to specify priors, simulate posterior distributions and check your models. You will then apply these ideas to linear models, generalised linear models, multilevel models and Bayesian measurement models.
Throughout the course, you will work with realistic social science examples and learn how to connect your modelling choices to theory, data and substantive interpretation. Conceptual discussion is closely linked to practical implementation, with hands-on data analysis in R and Stan.
By the end of the course, you will be able to explain the main differences and connections between Bayesian and frequentist inference, specify and estimate a range of common Bayesian models in Stan via R, interpret posterior quantities substantively, and assess model fit and uncertainty.
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Chendi works in comparative politics, political behaviour, political economy and political methodology. His research examines how citizens and political institutions respond to economic, geopolitical and technological change, with a particular focus on European politics.
His substantive work spans European integration, solidarity and collective capacity; public opinion and political behaviour; comparative political economy; party and electoral politics; and AI governance and algorithmic accountability. Methodologically, he uses and develops a range of approaches, including experiments, formal modelling, Bayesian and nonparametric statistics, measurement, time-series analysis, causal inference, machine learning, and AI and LLM auditing.
His Bayesian Modelling course won ECPR’s Cora Maas Award for the best-evaluated course at the 2023 Methods School
This course introduces you to Bayesian modelling for research in political science and the wider social sciences. Its central aim is to help you move from understanding the logic of Bayesian inference to building, estimating, interpreting and evaluating models that are useful for real research problems. To do this, the course combines conceptual discussion, computational practice in R and Stan, and substantive examples from social science research.
You will begin by exploring why Bayesian methods are useful in practice: they provide a coherent framework for handling uncertainty, allow you to incorporate prior information, and naturally connect estimation, prediction and model comparison. You will then be introduced to posterior inference, simulation-based computation, and the practical logic of Markov chain Monte Carlo and Hamiltonian Monte Carlo. These foundations underpin the rest of the course.
During the live 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 Moodle.
Introduce Bayesian inference, priors, posteriors and simulation-based estimation, and gets you started with the computational workflow in R and Stan.
Focus on the Bayesian linear model and models for binary outcomes, with an emphasis on interpretation and uncertainty, and on translating familiar regression ideas into the Bayesian framework.
Extend the framework to models for discrete choice and count outcomes, showing how Bayesian modelling can accommodate data structures common in political behaviour and public policy research.
Turn to hierarchical and multilevel models, with attention to partial pooling, cross-level structure and the kinds of comparative and panel data often found in political science. The session then introduces Bayesian measurement models as a bridge to latent variable analysis.
Cover model assessment and comparison, including posterior predictive checks and principled ways to evaluate model fit, complexity and substantive usefulness.
For the fourth credit, you will need to complete a research project applying the Bayesian methods covered in the course to your own data.
You must have a basic knowledge of statistical analysis, including linear regression models and hypothesis testing, and some exposure to models with limited dependent variables (e.g. binary outcomes). Completing Introduction to Inferential Statistics or Statistical Modelling with R will give you sufficient preparation.
The course mainly uses Stan, which you will access through R. Basic knowledge of R would be useful but is not required.
Before the course
You will need to complete 2–3 hours of reading before the course.
Learning commitment
The course includes three hours of live teaching each day for five days, alongside readings and other learning activities.
Once you have registered and paid, you will receive access to our Learning Management System (LMS) approximately two weeks before the course start date. Course materials, including pre-course readings, will be available here. Allow approximately 20 hours per week before the course to review the materials and complete readings and any pre-course tasks.
During the course week, you should allow approximately two to three hours per day for preparation and assignments, in addition to the live teaching sessions.
The course offers the opportunity to earn three ECTS credits. To earn a fourth credit, you will need to complete a post-course assignment involving approximately 25 hours of work.
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 prerequisite items. If in doubt, please contact us before registering.