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Member rate 2,713.79 zł
Non-Member rate 5,427.58 zł
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* If you attended a qualifying previous Methods School in 2025 or 2026, you qualify for 221.03 zł off your course fee.
Jagiellonian University: 8 – 11 September
Online: 14 – 15 September
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This course introduces Bayesian modelling for quantitative social science, with a particular focus on how Bayesian methods can be used to answer substantive questions. The course covers the logic of Bayesian inference, prior specification, posterior simulation, and model checking, and then applies these ideas to linear models, generalized linear models, multilevel models, and Bayesian measurement models. Throughout, you will work with realistic social-science examples and learn how to connect modelling choices to theory, data, and substantive interpretation.
By the end of the course, you will:
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
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. His current work is on European politics, comparative political economy of crisis and macro-policy, party and electoral politics, and political mobilisation.
Methodologically, his agenda emphasises Bayesian and non-parametric statistics, time-series analysis, measurement, machine learning and AI, and the integration of causal-inference and computational techniques.
He has published in the British Journal of Political Science, Western European Politics, Comparative European Politics, and in volumes published by Cambridge University Press.
This course provides an introduction to Bayesian modelling for researchers in political science and the wider social sciences. The 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.
The course combines conceptual discussion, computational practice in R and Stan, and substantive examples drawn from social-science applications. Starting with why Bayesian methods are useful in practice: they provide a coherent framework for uncertainty, allow the incorporation of 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 support the remainder of the course.
Introduction to Bayesian inference, priors, posteriors, and simulation-based estimation, alongside an orientation to the computational workflow in R and Stan.
Focusing on the Bayesian linear model and binary-outcome models, emphasising interpretation, uncertainty, and the translation of familiar regression ideas into the Bayesian framework.
Extending the framework to models for discrete choice and count outcomes, showing how Bayesian modelling can accommodate common data structures in political behaviour and public policy research.
Moving on to hierarchical and multilevel models, with attention to partial pooling, cross-level structure, and the kinds of comparative and panel data frequently encountered in political science; we then introduce Bayesian measurement models as a bridge to latent-variable analysis.
covers model assessment and comparison, including posterior predictive checks and principled ways to evaluate model fit, complexity, and substantive usefulness.
Throughout the course, participants will work with real-world data and case studies, and will be encouraged to apply the methods to their own research interests.
The course is structured into five live sessions, each lasting 3 hours. The first three sessions will take place from Tuesday 8 – Thursday 10 September at Jagiellonian University. The remaining two sessions will take place on Monday 14 – Tuesday 15 September, online. You must attend all sessions to complete the course.
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, consider taking courses such as Introduction to Inferential Statistics or Applied Regression Analysis.
This course will use Stan through R. Therefore it is prefered that you to have basic knowledge of R, though 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.
This course is designed at an Intermediate level.
Participants should expect approximately 24 hours of total engagement, including:
Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings.
You will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.
During the course week, you are expected to dedicate approximately two-three hours per day to prepare and work on assignments.
This comprehensive approach ensures that you not only attend the live sessions but also engage with the course material, participate actively, and complete assessments to solidify your learning.
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.