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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Monday 25 February – Friday 1 March, 09:00–12:30
15 hours over 5 days
Political scientists increasingly apply a Bayesian approach to diverse research topics. On this course, you will learn
The course consists of lectures (morning) and lab sessions (afternoon). The lectures deal with relevant background knowledge and specific skills for Bayesian analysis. In lab sessions, we apply these skills to political and social science data. Hence, you also learn basic JAGS and Stan, which are necessary for Bayesian estimation.
Tasks for ECTS Credits
2 credits (pass/fail grade)
3 credits (to be graded) As above, plus a short take-home exam.
4 credits (to be graded) As above, plus a long take-home exam.
Susumu Shikano is Professor of Political Methodology at the University of Konstanz. His research interests are spatial models of politics and various topics in political behaviour.
His work has appeared in journals including Public Choice, Political Psychology, Party Politics, West European Politics, and the British Journal of Political Science.
Political scientists increasingly apply Bayesian analysis to diverse kinds of research topics, thanks to its ability to handle aggregate data without sampling processes, to analyse small-N data, and to estimate models with complex likelihood functions. The increased capacity of modern computers enables a wider range of researchers to conduct such computationally intensive estimations.
Despite these advantages there are still several points that need addressing:
The course aims to close these gaps, by:
The course covers:
The course consists of lectures and lab sessions. The lectures deal with relevant background knowledge as well as specific skills for Bayesian analysis. In lab sessions, we will apply these skills to political and social science data. Hence, you'll also learn basic JAGS and Stan, which are necessary to conduct Bayesian estimation using MCMC.
You should have knowledge of statistical analysis including regression models with different types of dependent variables, and maximum likelihood estimation (MLE). At very least, you should know how to calculate the likelihood of certain parameter sets of a statistical model given observed data. In lab sessions we will learn how to use JAGS from R, so basic knowledge in R is also required.
This is an introductory one-week course. We cannot thoroughly treat the wide range of statistical models and further advanced topics in Bayesian statistics.
If you already have basic knowledge of Bayesian statistics and can conduct regression analysis using JAGS, do not take this course.
Statistics, including regression models with different types of dependent variables.
Maximum likelihood estimation (MLE). In particular, you should be able to distinguish likelihood.
Basic knowledge of R.
Day | Topic | Details |
---|---|---|
1 | General introduction, Basics of Bayesian inference, Conjugacy analysis, Introduction to JAGS and Stan. |
1.5 hours lecture 1.5 hours lab time on R programming |
2 | Linear and discrete regression models, Markov-Chain-Monte-Carlo |
1.5 hours lecture 1.5 hours lab time on R programming |
4 | Advanced regression models |
1.5 hour lecture 1.5 hour lab time on R programming |
5 | Further advanced topics (model selection, Bayes factor, model averaging and data augmentation) |
1.5 hours lecture 1.5 hours lab time on R programming |
3 | Various topics concerning priors |
1.5 hours lecture 1.5 hours lab time on R programming |
Day | Readings |
---|---|
1 |
Shikano (2014), Gill (2014) Chapters 1–2 and 11, Lambert (2018) Chapters 2–7, 16 |
2 |
Shikano (2014), Gill (2014) Chapters 5 and 10, Lambert (2018) Chapters 12–15 |
3 |
Gill (2014) Chapter 4, Lambert (2018) Chapters 5, 9 and 11 |
4 |
Gerlman / Hill (2007) Chapter 6 |
5 |
Gill (2014) Chapters 6–7, Lambert (2018) Chapter 10 |
Please bring your own laptop.
Gelman, A. and Hill, J.
Data Analysis using Regression and Multilevel/Hierarchical Models
Cambridge University Press; 2007
Gill, Jeff
Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition
Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences; 2014
Lambert, Ben
A Student’s Guide to Bayesian Statistics
Sage; 2018
Shikano, Susumu
Bayesian estimation of regression models
in Henning Best and Christof Wolf, eds. Regression Analysis and Causal Inference, Sage, 2014; p.31–54
Summer School
Introduction to R
Winter School
Introduction to Maximum Likelihood Estimation
Introduction to R
Winter School
Multilevel Regression Modelling
Inferential Network Analysis
Introduction to Machine Learning for the Social Sciences