STAT8310 - Bayesian Data Analysis
Preface
Description
This course will cover the topics in the theory and practice of Bayesian statistical inference, ranging from a review of fundamentals to questions of current research interest. Motivation for the Bayesian approach. Bayesian computation, Monte Carlo methods, asymptotics. Model checking and comparison. A selection of examples and issues in modelling and data analysis. Discussion of advantages and difficulties of the Bayesian approach. This course will be computationally intensive through analysis of data sets using the R statistical computing language.
Prerequisites
MATH 4752/6752 – Mathematical Statistics II or equivalent, and the ability to program in a high-level language.
Instructor
Chi-Kuang Yeh, Assistant Professor in the Department of Mathematics and Statistics, Georgia State University.
- Office: Suite 1407, 25 Park Place.
- Email: cyeh@gsu.edu.
Office Hour
10:00–13:00 on Monday, or by appointment.
Grade Distribution
- Homework – 50%
- Exam – 30%
- Final – 20%
Assignment
Midterm
Topics and Corresponding Lectures
Those chapters are based on the lecture notes. This part will be updated frequently.
| Status | Chapter | Topic | Lecture |
|---|---|---|---|
| ✅ | Ch. 1 | Welcome and Overview | 1 |
| ✅ | — | Intro to R Programming | 2 |
| ✅ | Ch. 2 | Probability and Exchangeability | 3–5 |
| ✅ | Ch. 3 | One Parameter Models | 6–10 |
| 🛠️ | Ch. 4 | Monte Carlo | 11– |
Recommended Textbooks
Gelman, A., Carlin, J., Stern, H., Rubin, D., Dunson, D., and Vehtari, A. (2021). Bayesian Data Analysis, CRC Press, 3rd Ed.
Hoff, P.D. (2009). A First Course in Bayesian Statistical Methods, Springer.
McElreath, R. (2018). Statistical Rethinking: A Bayesian Course with Examples in R and Stan, CRC Press.
Side Readings
- TBA