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Introduction to Bayesian Inference

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

Susumu Shikano

susumu.shikano@uni-konstanz.de

Universität Konstanz

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.


Instructor Bio

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.

 @SusumuShikano

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.

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

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

Summer School

Introduction to R

Event History and Survival Analysis

Advanced Topics in Applied Regression Multiple Regression

Multiple Regression

 

Winter School

Introduction to Statistics

Introduction to R

Recommended Courses to Cover After this One

Summer School

Advanced Discrete Choice Modelling

Panel Data Analysis

Handling missing data

 

Winter School

Advanced Discrete Choice Modelling

Panel Data Analysis

Handling missing data