Bayesian Analysis: An Introduction


This workshop covers the theoretical foundations of the Bayesian modeling paradigm as well as the basics of estimation. The course will focus primarily on the application of Bayesian statistical models to social science data, as well as interpretation and presentation of results.

Each day the course will cover a variety of topics. On some days, the lecture will end around 3:30pm and then participants will work on problem sets, with the instructor available to help participants write and execute code and generate presentable results. The goal of the week is to have participants leave with a suite of code that will allow them to estimate a variety of models on their own.

Prerequisites: While no prior knowledge of Bayesian statistics is expected, the course does assume a working knowledge of ordinary least squares (OLS) and maximum likelihood estimation (MLE) modeling.

Software: The course also does not assume prior working knowledge of the software, which will be R, OpenBUGS, and JAGS. However, participants are encouraged to gain some familiarity with the R statistical environment prior to the course.

R is free and can be downloaded from the Comprehensive R Archive.

OpenBUGS is free and available at the BUGS Project site.

JAGS is free and available at the JAGS site.

Mac users will need to use JAGS and R. Windows users can use either OpenBUGS or JAGS, and will also need R. While there are other options to estimate models in a Bayesian framework (including STAN and newer versions of Stata), this course will focus on BUGS/JAGS.

Fee: Members = $1600; Non-members = $3000

Tags: Bayes, Bayesian Analysis

Course Sections