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.

The laboratories will focus on the R software and their utility is twofold. On the one hand, they consolidate the understanding of the theoretical topics. On the other hand, they provide guidance on using the R software and dedicated packages to implement, fit, and interpret the Bayesian models applied to data from social sciences. Part of the laboratories will be dedicated to hands-on group work based on real datasets. The results will be presented by the students in front of the class and jointly discussed. Students can bring their own datasets.

Detailed lecture plan (daily schedule)

Day 1.
All day: Introduction to the Bayesian modeling framework. The concepts of priors and posterior distributions. Some notable examples of conjugate priors.

Day 2.
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.
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.
Morning: Bayesian model-based clustering via mixture models, Challenges and estimation strategies
Afternoon: LAB 3: practical implementation - Hands-on session 3

Day 5.
Morning: Advanced Bayesian modeling 1: ABC and Bayes for network data
Afternoon: Group presentations

*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 is professor of statistics, founder and director of the Data Science Lab at Università della Svizzera italiana.Francesco holds the role of Assistant Professor (Rtd-A) at the Department of Statistics of Università Cattolica del Sacro Cuore.

Target group

graduate students, doctoral researchers, early career researchers

Prerequisites

The course assumes a basic familiarity with probability theory and with linear regression analysis. A good knowledge of R is essential for the successful completion of the course

Fee info