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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 6 to Friday 10 March 2017
Generally classes are either 09:00-12:30 or 14:00-17:30
15 hours over 5 days

Prerequisite Knowledge

Prior knowledge of statistics including regression models with different types of dependent variables. Basic knowledge of R.


Short Outline

Political scientists increasingly apply the Bayesian approach to diverse kinds of research topics. To motivate further political scientists to use this approach, this course provides participants the following three points: First, the course provides a well-grounded conceptional background for Bayesian inference. Second, participants will be guided how to read the literature concerning Bayesian statistics and interpret the results. Third, this course gives a practical introduction to a software for Bayesian analysis with political science examples. The course consists of lectures (morning) and lab sessions (afternoon). The lecture deals with relevant background knowledge as well as specific skills for Bayesian analysis. In lab sessions, these skills are applied to political and social science data. Hence, course participants also learn the basic knowledge of JAGS, which is needed to conduct Bayesian estimation.


Long Course Outline

Political scientists increasingly apply the Bayesian approach to diverse kinds of research topics. This development is due to a series of its attractive features: e.g. handling aggregate data without sampling processes, analyzing small N data, estimating models with complex likelihood functions. Furthermore, the increasing capacity of modern computers enables a wider range of researchers to conduct such computationally intensive estimations.

Despite these advantages there is still backlog demand in respect to several points: First, it is not widely enough acknowledged that Bayesian statistics and conventional statistics are based on different views concerning theory and data. Second, the literature, including text books, is in general too technical to motivate most political scientists to apply Bayesian analysis to their own research questions. Third, the programs needed for Bayesian analysis is not user friendly enough for most political scientists.

The course aims to close these gaps. First, the course provides a well-grounded conceptional background for Bayesian analysis. Second, participants will be guided how to read the literature concerning Bayesian statistics and interpret the results. Third, this course gives a practical introduction in a specific software for Bayesian analysis with political science examples.

More specifically, the course covers the following topics: Fundamentals of Bayesian analysis, Bayesian estimation using MCMC and estimation of various regression models (binary logit/probit, poisson, multi-level, robust regression etc.) in Bayesian framework. The course consists of lectures (morning) and lab sessions (afternoon). The lecture deals with relevant background knowledge as well as specific skills for Bayesian analysis. In lab sessions, these skills are applied to political and social science data. Hence, course participants also learn the basic knowledge of JAGS which is needed to conduct Bayesian estimation.

Participants are required to have basic knowledge in statistical analysis including regression models with different types of dependent variables. Furthermore, in lab sessions participants learn how to use JAGS from R. Therefore, the basic knowledge in R is also required.

Note that this is an introductory one week course. This course, therefore, cannot thoroughly treat the wide range of statistical models and further advanced topics in Bayesian statistics. For those who have basic knowledge in Bayesian statistics and can conduct regression analysis using JAGS, this course is not adequate.

Day Topic Details
1 General introduction, Basics of Bayesian inference 1.5 hours lecture, 1.5 hours lab time on R programming
2 Markov-Chain-Monte-Carlo Introduction to JAGS 1.5 hours lecture, 1.5 hours lab time on R programming
3 Regression models for discrete dependent variables

1.5 hours lecture, 1.5 hours lab time on R programming

4 Further regression models

1.5 hours lecture, 1.5 hours lab time on R programming

5 Multi-level models

1.5 hours lecture, 1.5 hours lab time on R programming

Day Readings
1

Shikano (2014), Jackman (2009) Chapter 1-2

2

Shikano (2014), Jackman (2009) Chapter 3-6

3

Shikano (2014), Jackman (2009) Chapter 8

4

Shikano (2014)

5

Gelman & Hill (2007)

Software Requirements

R (free from http://cran.r-project.org/)

JAGS (free from http://mcmc-jags.sourceforge.net/)

Hardware Requirements

Students bring their own laptops.

Literature

Shikano, Susumu, Bayesian estimation of regression models, Henning Best and Christof Wolf, eds. Regression Analysis and Causal Inference, Sage, 2014; p.31-54

Jackman, S. Bayesian Analysis for the Social Sciences. Wiley: New York. 2009.

Gelman, Andew and Hill, Jennifer. Data Analysis using Regression and Multilevel/Hierarchical Models. Cambridge University Press; 2007.

 

 

 

Recommended Courses to Cover Before this One

<p><strong>Summer School</strong></p> <p>Introduction to R</p> <p>Event History and Survival Analysis</p> <p>Advanced Topics in Applied Regression Multiple Regression</p> <p>Multiple Regression</p> <p>&nbsp;</p> <p><strong>Winter School</strong></p> <p>Introduction to Statistics</p> <p>Introduction to R</p>

Recommended Courses to Cover After this One

<p><strong>Summer School</strong></p> <p>Advanced Discrete Choice Modelling</p> <p>Panel Data Analysis</p> <p>Handling missing data</p> <p>&nbsp;</p> <p><strong>Winter School</strong></p> <p>Advanced Discrete Choice Modelling</p> <p>Panel Data Analysis</p> <p>Handling missing data</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.