Advanced Special Topics in Biostatistics: "Advanced introduction to Bayesian statistics" (Biostatistics 140.850, 1st Term, 2023-24)
This course provides an advanced introduction to Bayesian methods for students with a solid foundation in statistics and calculus-based probability. The course assumes no prior exposure to Bayesian paradigms but, by building on knowledge of classical statistics, proceeds more quickly than a typical first course on Bayesian statistics. Students are expected to be familiar with statistics and probability at the level of 651–654/751–754 and 646–649/721–724 and in particular with the following topics: hypothesis testing, maximum likelihood estimator and its asymptotic distribution, regression models, regularization and bias-variance trade-off, and random effect models. Similarities to and differences from frequentist paradigms are explored whenever appropriate. The goal is to 1) introduce Bayesian concepts and machinery of general interest regardless of whether one subscribes to Bayesian philosophy and 2) provide a foundation for further (self-) studies of more advanced Bayesian methods.