STA 602 - Intro to Bayesian Statistics (Fall 2023)

Lecture: Tue and Thu 1:25-2:40pm in Old Chem 101.
Lab (starting from Week 3): Mon 3:05-4:20pm (01L) in Reuben-Cooke 129 or 4:40-5:55pm (02L) in Reuben-Cooke 127.

Instructional Crew

Li Ma (Instructor), Email:

Aihua Li (TA), Email:

Minhui Jiang (TA), Email:

Don't forget to remove the arctic birds from the email addresses!

Office hours

Mon: 6-7pm (Aihua) Old Chem 203B and on Zoom.
Tue: 4:30-6:30pm (Minhui) Old Chem 025 and on Zoom.
Wed: 5-7pm (Aihua) Old Chem 025 and on Zoom.
Fri: 2:15-3:15pm (Li) On Zoom.

All office hours share the same Zoom link, which will be distributed on Sakai.

Required text:

Peter Hoff, A First Course in Bayesian Statistical Methods, 1st edition, ISBN-10: 0387922997. (This text is free for download on SpringerLink when you are on Duke campus network or VPN.)

Course description

This course presents an introduction to the concepts and methods of Bayesian inference, with a focus on both modeling and computation.


Linear algebra, multivariate calculus based probability theory at the level of STA 230, and linear regression models at the level of STA 211. For a quick check on your probability background try this old exam.


Lecture notes will be distributed on Sakai.

Week Topics (might be adjusted as we go along) Readings in textbook
1 Course overview, Inference using Bayes theorem Ch1-2
2 Exponential family and conjugate models Ch2.7-3, Ch5.1-5.2
3 Loss function and Bayes estimation
4 Monte Carlo approximation, Quiz 1 Ch4
5 Rejection sampling and importance sampling, non-informative priors Ch4
6 Univariate normal model and Gibbs Sampling Ch5-6
7 Review and Midterm exam
8 MCMC diagnostics, multivariate normal model Ch6-7
9 Multivariate normal model (cont'ed) Ch7
10 Missing data and imputation, hierarchical models, Quiz 2 Ch7-8
11 Hierarchical models, graphical models, Ch9-10
12 Bayesian linear models, hypothesis testing, model choice Ch10
13 Metropolis-Hastings algorithms, review
Final exam (tentatively on Thursday December 7)


Weekly homeworks will be graded on a 4-point scale (Excellent, Good, Fair, and Poor). Both an Excellent and a Good will give you full credit for grading purposes. You must show your work to receive credit. Late homeworks will be accepted, but will incur a one-level grade penalty for each 24-hour period it is late (starting from the minute past the deadline). The lowest homework grade will be dropped. Homeworks are to be released and submitted on Gradescope.


There will be a total of 9 to 10 weekly lab sessions on Mondays starting starting from the second week. The main purpose of the lab is to provide you with a training in implementing Bayesian methods in R. Occasionally the lab might be used for reviews. Lab reports are to be submitted at the end of each lab. Grading on the labs is only based on attendance and participation.


There will be two in-class 30-minute closed-book quizzes.


There will be a midterm exam and a cumulative final, both in-class and close-book/notes. For the exams, you may bring a calculator and a letter sized cheat-sheet (which may be 1-sided for the midterm and 2-sided for the final).

If you have an unchangeable conflict with any of the exams and quizzes, you must submit the appropriate university online form (NOVAP, RHoliday) and arrange with me at least one week prior to the scheduled exam or quiz. The make-up exam or quiz in such cases will typically occur before the scheduled one. If you miss the midterm or a quiz due to short-term illness, you should submit the online Incapacitation Form before the exam or quiz, and email me before the exam or quiz. In such cases your final will count for both the missed exam or quiz and the final.


The course grade will consist of weekly homework (10%), weekly labs (10%), quizzes (15% total), the midterm (30%), and the final (35%). All grades are reported on Gradescope.


Small (online or in-person) study groups are encouraged.

Discussions on the homework problems are allowed, but you must write down your own solutions independently.

Academic integrity

Each student is committed to Duke's Community Standard. No form of academic dishonesty will be tolerated. Some examples include cheating, plagiarism, and lying about illness or other reasons for absence. Violations of the standard will result in an automatic F of this course and will be reported to the Office of Student Conduct.