Short Course in Hierarchical Modeling

Series of two 2.5 hours lectures given in the Special Topics in Political Methodology class in the Department of Political Science. Covered the key ideas of hierarchical modeling, including some discussion of Bayesian prior distributions. Focused on the theoretical intuition, computer implementation (including WinBUGS/JAGS), and presentation of results.

Spring 2012

Fall 2012

Day 1

Day 2 - Load the script SamplingIllustrated.R directly into R and copy and paste the Initial Problem and Real Problem script into separate RStudio tabs.

 

Other Useful Resources

  • Justin Esarey's lecture on random effects models
  • In Fall 2012, Justin is teaching a really nice class on computational methods. He has a variety of lectures, scripts, and notes for the class here, covering topics such a Bayesian hierarchical models and implementation in BUGS/JAGS.