Bayesian Modeling and Inference

Undergraduate

This course offers a rigorous and comprehensive introduction to Bayesian modeling and inference, encompassing foundational theory, modern computational techniques, and advanced modeling frameworks. The objective of this course is to provide students with a deep and practical understanding of Bayesian modeling and inference. By the end of the course, students are able to explain the Bayesian paradigm, including the roles of prior distributions, likelihoods, and posterior distributions, and compare Bayesian and frequentist approaches. It is designed for advanced undergraduates, master’s students, and PhD students with a strong interest in statistical modeling, inference, and applications across diverse disciplines. Probability theory at the level of S&DS 2410 / S&DS 5410 , statistical inference at the level of S&DS 2420 / S&DS 5420 , linear algebra at the level of MATH 2250 or MATH 2260 or equivalent, and experience with computing and data analysis in common programming languages such as R and Python. TTh 1:05pm-2:20pm