Course Syllabus

Thursdays 6:40 - 9:30pm (GMT-5), Hill Center 552 (from Feb and on; remote via Zoom in January)

Course Description: An introduction to Bayesian statistical modeling, inference, and computation. Single- and multi-parameter models, hierarchical models, model checking, evaluation, selection, sensitivity analysis and prediction. Bayesian decision analysis. Monte Carlo and Markov chain Monte Carlo (Metropolis-Hastings, Gibbs). Select topics in advanced Bayesian computation, e.g. Hamiltonian Monte Carlo and approximate Bayesian computation.

Instructor: Ruobin Gong (ruobin.gong@rutgers.edu)

Course Syllabus (v. 01/18/2022)