Bayesian Modeling and Inference

Graduate / Professional

This course offers a rigorous and comprehensive introduction to Bayesian modeling and inference, encompassing foundational theory, modern computational techniques, and advanced modeling frameworks. It is designed for advanced undergraduates, master’s students, and Ph.D. students with a strong interest in statistical modeling, inference, and applications across diverse disciplines. Prerequisites: Probability theory at the level of S&DS 2410 /5410, statistical inference at the level of S&DS 2420 /5420), linear algebra at the level of MATH 2250 or 2260 , and experience with computing and data analysis in common programing languages such as R and Python. TTh 1:05pm-2:20pm