ECPR

Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

ECPR

Install the app

Install this application on your home screen for quick and easy access when you’re on the go.

Just tap Share then “Add to Home Screen”

Back to Panel Details
Back to Panel Details

Introduction to Bayesian Inference

Susumu Shikano
susumu.shikano@uni-konstanz.de

Universität Konstanz

Susumu Shikano is Professor of Political Methodology at the University of Konstanz. He is also affiliated with the Mannheim Centre for European Social Research and the Hanse Institute for Advanced Study in Delmenhorst.

His research interests are various topics in electoral politics, coalition formation and methodology.

Susumu has published articles in journals including Public Choice, Political Psychology, Party Politics, West European Politics, and the British Journal of Political Science.

  @SusumuShikano


Course Dates and Times

Monday 25 February – Friday 1 March, 09:00–12:30
15 hours over 5 days

Prerequisite Knowledge

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.


Short Outline

Political scientists increasingly apply a Bayesian approach to diverse research topics. On this course, you will learn

  1. a conceptual background to Bayesian inference
  2. how to read the literature using Bayesian statistics, and how to interpret the results
  3. an introduction to a software for Bayesian analysis, with political science examples.

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) 

  • Attend at least 90% of course hours
  • Participate fully in in-class activities
  • Carry out the necessary reading and/or other work prior to, and after, class
  • Submit daily assignments.

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.


Long Course Outline

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:

  • it is not sufficiently acknowledged that Bayesian statistics and conventional statistics are based on different views concerning theory and data
  • the literature, including textbooks, is in general too technical to motivate most political scientists to apply Bayesian analysis to their own research questions
  • the programs needed for Bayesian analysis are not user-friendly enough for most political scientists.

The course aims to close these gaps, by:

  1. providing a conceptual background for Bayesian analysis
  2. showing you how to read the literature using Bayesian statistics, and interpret the results
  3. introducing you to software for Bayesian analysis, with political science examples.

The course covers:

  • fundamentals of Bayesian analysis
  • estimation of linear regression models using conjugacy and Markov Chain Monte Carlo (MCMC)
  • estimation of various regression models (binary logit/probit, poisson, robust regression, etc) and further topics (model selection and check, data augmentation, etc) in Bayesian framework.

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.

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

Software Requirements

Download R 

Download JAGS

Download rstan

Hardware Requirements

Please bring your own laptop.

Literature

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

Recommended Courses to Cover Before this One

<p><strong>Summer School</strong></p> <p>Introduction to R</p> <p><strong>Winter School</strong></p> <p>Introduction to Maximum Likelihood Estimation</p> <p>Introduction to R</p>

Recommended Courses to Cover After this One

<p><strong>Winter School</strong></p> <p>Multilevel Regression Modelling</p> <p>Inferential Network Analysis</p> <p>Introduction to Machine Learning for the Social Sciences</p>


Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Conveners

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.