An Introduction to Bayesian Methods for the Social Sciences

About

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

Day 1 – Monday (all day)
Introduction to the Bayesian modeling framework. The concepts of priors and posterior distributions. Some notable examples of conjugate priors: inference on proportions (Bernoulli model) and means (Gaussian model).

Day 2 – Tuesday
Morning

Methods for posterior simulation: Monte Carlo and Monte Carlo Markov Chains.
Afternoon

LAB 1: R basics, conjugacy, basic model estimation, MCMC foundations, Stan – Hands-on session 1
Day 3 – Wednesday
Morning

Bayesian linear regression
Bayesian logistic regression
Afternoon

LAB2: practical implementation. Shrinkage priors for variable selection: the Bayesian Lasso and the Horseshoe prior – Hands-on session 2
Day 4 - Thursday
Morning

Bayesian model-based clustering via mixture models
Challenges and estimation strategies
Afternoon

LAB 4: practical implementation – Hands-on session 3
Day 5 – Friday
Morning

Advanced Bayesian modeling. Specific topics will be selected by discussing with the class
Afternoon

Group presentations
Course feedback and Q&A

*The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.**

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you.

Course leader

Antonietta Mira is professor of statistics, founder and director of the Data Science Lab at Università della Svizzera italiana. Francesco Denti is a Senior Assistant Professor (Rtd-B) at the Department of Statistics of the University of Padua.

Target group

graduate students, doctoral researchers, early career researchers

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.

Fee info