Winter 2024 STAT 27410-1

In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. These methods, combining information from similar and independent experiments and yield improved estimation of both individual and shared model characteristics, have been widely applied in many fields such as biomedical science, public health, epidemiology, education, social science, ecomnomics, psychology, agriculture and engineering. In this course, we will introduce Bayes and EB methods, as well as the necessary tools needed to evaluate their performances comparing with the frequentist methods. For computation, we will introduce Markov chain Monte Carlo methods such as the Gibbs sampler algorithm. We will use R and RStan to implement these methods and solve real world problems.

Students in this class are required to do final projects in small groups. During the last week of the quarter, each group will have the opportunity to present the final project to the class. Final reports based on the group projects will be due by the end of the exam week. Due to the attention required from the instructor to supervise the final projects, the class size will be capped at the enrollment limit.