STAT8310 - Bayesian Data Analysis

Preface

Thank you

The course is finished (except of the final project). Thank you everyone for the participation and attention. Hope you all have learned something from this course, and have fun. Wish everyone has a good summer vacation! 🏀😼😊

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

• A1, due on Jan 29, 2026
• A2, due on Feb 13, 2026
• A3, due on Mar 01, 2026
• A4, due on Mar 25, 2026
• A5, due on Apr 20, 2026

Midterm

• March 5, 2026

Final Project

• Due on May 1, 2026

More information can be found on the project page

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	6–9
✅	Ch. 4	Monte Carlo	10– 13
✅ ️	Ch.5	Gibbs Sampler	14,17–18, 20
✅		Midterm Review	15
✅		Midterm Exam	16
		⛱ Spring Break 🌊
✅	-	Intro to JAGS and BUGS	19
✅	Ch.6	MCMC Diagnosis	20-22
✅	Ch.7	Multivariate Gaussian	22– 26
✅	Ch. 8	Select topic: Bayesian Machine Learning:	27
✅	Ch. 9	Select topic: Modern Bayesian Computation	28

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

• Just Another Gibbs Sampler Page
• BUGS and Winbugs from MRC Biostatistics unit Page