Political Science 797BA - ST-Topics/BayesianAnlysis&Stat

This course will introduce the fundamentals of applied Bayesian data analysis for social scientists - including model development, estimation, quantification of uncertainty, and model checking - as well as a few key notions from statistical machine learning. Emphasis will be on acquiring basic computational skills needed by practitioners and on interpretation of results. Topics will vary, but may be drawn from multilevel regression and generalized linear models (HLMs/GLM), classification, clustering, measurement models, approaches to missing data, categorical variable analysis, and causal inference. The primary computing environment will be R, with additional exposure to specialized tools for Bayesian computation (e.g. Stan, BUGS, JAGS). Some previous exposure to R is recommended, but not required.

Open to Graduate students only.