The goal of this course is to give students an overview of the theory and methods for data analyses using the Bayesian paradigm. Topics include: (1) foundations of Bayesian inference; (2) development of Bayesian models and prior choices; (3) analytical and simulation techniques for posterior estimation; (4) model choice and diagnostics; (5) sensitivity analysis, and; (6) introduction to Monte Carlo Markov Chain (MCMC) simulations. Students will also learn how to estimate and summarize Bayesian models using Bayesian statistical packages (R/JAGS/Bugs). The course will use working examples with real application of Bayesian analysis in social sciences. Prerequisites: Basic knowledge of probability (e.g., joint and conditional distributions, expectation, variance) and introductory-level experience with R or Python (Note: Open to Advanced Undergraduates with Instructor Permission)
See course webpage