| Description |
An introduction to the basic analytical and computational tools of applied Bayesian statistics. Methods covered include multi-level models, mixture modeling, Bayesian model averaging, and models for missing data and causal inference; computational tools taught include the EM algorithm and the Markov chain Monte Carlo algorithms. Goal of the course is to enable students to build and implement their own model in order to answer a particular research question. Course may be of interest to those in disciplines outside of political science who need to learn the basics of applied Bayesian statistics. |