An Introduction to Bayesian Methods for the Social Sciences

Lecturers: Antonietta Mira & Francesco Denti

Modality: In-presence

Week 2: 17-21 August 2026

Workshop contents and objectives

Bayesian statistics has experienced a surge in popularity over the last few decades, primarily due to the computational advancements that have mitigated its traditionally perceived complexity. The progressive expansion of the Bayesian method has allowed practitioners to embrace its intuitive, probabilistic reasoning and leverage its flexibility in formulating elaborate models for real-world data.

This course aims to give participants a simple but rigorous foundation of Bayesian Statistics. Our program is designed to start from the fundamental concepts and progress to developing simple and advanced models explicitly tailored for applications in the social sciences.

The course will cover essential topics, starting with the basics of Bayesian inference, including posterior distribution, estimation, credible intervals, and hypothesis testing. Moving forward, we will explore specific areas such as:

• Regression Models and Variable Selection: We will discuss the basic regression models and then discuss the use of priors for variable selection.
• Models for Network Data: We will delve into the application of Bayesian statistics for modeling and interpreting network data, providing insights into the dynamics of interconnected systems.
• Model-Based Clustering: This section will cover model-based clustering, a technique crucial for segmenting complex datasets into homogeneous groups. This approach facilitates a nuanced understanding of patterns within diverse datasets.

Workshop design

The course is carefully structured to maintain a balanced approach, incorporating both theoretical classes and hands-on practical laboratories. This dual strategy aims to provide participants with a comprehensive understanding of the reliability and practical applications of Bayesian statistics. Engaging in both theoretical concepts and practical applications will enable attendees to gain valuable insights into the theory and the real-world applicability of Bayesian statistical techniques.

More specifically, during the theoretical classes, the basics of Bayesian modeling will be covered, and essential methods will be introduced and described. For each topic, we will draw parallels with the more common frequentist statistics, highlighting differences in methodology and interpretation and the pros and cons of the two alternative approaches. The laboratory sessions will utilize the R software and serve two primary purposes. First, they will consolidate the understanding of theoretical concepts. Second, they will provide hands-on guidance for using R and its dedicated packages to implement, fit, and interpret Bayesian models with social science data. Students are strongly encouraged to bring their own data for analysis, which will be used for practical, real-world examples and discussion. The results will be presented by the students in front of the class and jointly discussed.

Detailed lecture plan (daily schedule) tentative

Prerequisites

Participants should have a foundation in probability theory and linear regression. Familiarity with frequentist inference - including point estimation, hypothesis testing, and confidence intervals - is also expected. Proficiency in R is essential for the successful completion of the course.

Recommended readings or preliminary materials

• Alicia A. Johnson, Miles Q. Ott, Mine Dogucu, “Bayes Rules! An Introduction to Applied Bayesian Modeling”, ISBN 9780367255398 by Chapman & Hall https://www.bayesrulesbook.com

What our participants appreciated most

"| really liked it. The instructors were very responsive and took the flow based on the students. This requires the audience to be experienced and active learners though. From a non-mathematical background, | was able to follow and catch up with the course due to the active learning style, and I've benefited a lot."

"The workshop met my expectations by providing detailed explanations of Bayesian statistics, from its basic concepts to more advanced methods, and demonstrating how they can be applied to the social sciences. | particularly enjoyed the hands-on examples that we implemented in R Studio with the guidance of the instructors and support from colleagues."