Course: STAT 27410
Title: Introduction to Bayesian Data Analysis
Instructor(s): Fei Liu
Teaching Assistant(s): Daniel Xiang
Class Schedule: Section 1: MWF 11:30 AM-12:20 PM (Remote)
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.
Prerequisite(s): [STAT 22000 or STAT 23400 or STAT 22400 or STAT 22600 or STAT 24500 or STAT 24510[ AND [(MATH 13200 or MATH 15200 or MATH 15300 or MATH 16200 or MATH 16210 or MATH 15910 or MATH 19520 or MATH 19620 or MATH 20250 or MATH 20300 or MATH 20310) with a grade of C+ or higher]