Course: STAT 27410
Title: Introduction to Bayesian Data Analysis
Instructor(s): Fei Liu
Class Schedule: Sec 1: MWF 11:30 AM-12:20 PM in Stuart 104
Textbook(s): Gelman, Carlin, Sten, et al., Bayesian Data Analysis (3rd ed)
Description: In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. These methods combine information from similar and independent experiments and yield improved estimation of both individual and shared model characteristics. In this course, we introduce Bayes and EB methods, as well as the necessary tools needed to evaluate their performances relative to traditional, frequentist methods. We shall focus on more practical, data analytic and computing issues. Various computing methods will be discussed, in order to find the posterior distributions, including Markov chain Monte Carlo methods such as the Gibbs sampler. We will use R to implement these methods to solve real world problems.
The methods will be illustrated from applications in various areas, such as biological science, biomedical science, public health, epidemiology, education, social science, economics, psychology, agriculture and engineering. Recent developments of Bayesian methods on nonlinear models, longitudinal data analysis, hierarchical models, time series, survival analysis, spatial statistics will also be explored.
Notes: Students should be comfortable with coding in R software.