The course covers Bayesian statistical methods for inference and prediction including: estimation; model selection and prediction; exchangeability; prior, likelihood, posterior and predictive distributions; coherence and calibration; conjugate analysis; Markov Chain Monte Carlo methods for simulation-based computation; hierarchical modeling; Bayesian model diagnostics, model selection and sensitivity analysis.

Instructor: Prof. Raquel Prado BE-365C

Class time and location: Tu-Th 10-11:45am Thimann Lab 101

Office Hours: Mon 3-4pm and Th 1-2pm