Bayesian Modelling

online 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.

We will use JAGS (or BUGS) and Stan through R. We would therefore prefer 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.
In Depth
To get the most out of this course, you will need to complete the required in-depth readings for each day, and skim at least one of the recommended readings, if listed.

Five pre-recorded lectures, introducing the course's major topics and concepts, supplement the readings.

Key topics covered
Monday
Why Bayesian? Bayesian inference concepts, simulation-based inference and MCMC.

Tuesday
The linear model, and models for binary outcome.

Wednesday
Discrete choice outcomes and count outcomes.

Thursday
Hierarchical models and measurement models.

Friday
Model assessment and comparison.

How the course will work online
The course consists of a 3-hour live Zoom session on each of the five days. These sessions focus on two tasks:

• theoretical considerations behind different models
• guided hands-on coding illustration embedded in the lecture
The hands-on coding will enable you to master the technical side of Bayesian modelling in R. These sessions will also enable you to apply the statistical methods we discuss during lectures to real-world data.

The Instructor will distribute the slides and R script in advance so you can explore the code at your own pace, but we will go through the code and models together during the sessions.

We will get to know each other, and each other's projects, and explore how we can apply Bayesian modelling to answer your research questions. There will also be problem sets after each session. We will discuss these assignments, and any problems you may have, together the following day.

You can share thoughts and ask questions on Canvas, and the Instructor will host live Q&A sessions. You will be able to sign up for a quick one-to-one consultation during designated office hours."

Course leader

Alexandru Moise is a postdoctoral researcher at the European University Institute (2020–2025). He received his PhD in political science from Central European University in 2019. Alex's research focus is the political economy of welfare reforms.

Target group

The course is suitable for researchers, professional analysts, and advanced students.

Course aim

"The course will teach you to understand and apply various Bayesian methods for answering research questions in quantitative social science. In addition to the theoretical material, you will gain proficiency in data analytic skills by using the open-source statistical programming language R.

This is an advanced course for students who already have basic quantitative methods training.

By the end of the course you will:

-understand the fundamental differences and similarities between frequentist and Bayesian approaches to inference
-be able to formulate linear and generalised linear models, hierarchical models and measurement models in the Bayesian framework using JAGS or Stan
-know how to interpret models in the Bayesian framework.
You will also be able to compare and assess Bayesian models and apply the Bayesian methods to political science research questions."

Credits info

4 EC
"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."

Fee info

GBP 492: ECPR Member - check if your institution is a member on our website.
GBP 985: ECPR Non-Member

Scholarships

Funding applications for the 2024 ECPR Methods School Winter instalment are now closed. For more details on funding opportunities for ECPR's other events, please visit our website.