Bayesian Modelling

About

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.

Course leader

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.

Target group

Social science researchers

Course aim

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.

Fee info