Course Overview

Bayesian methods are increasingly important in both industry and academia. This is a graduate-level course that introduces students to the basics of Bayesian inference and provides students with the tools needed to fit Bayesian models.

In this course, you will learn the importance of Bayesian methods and inference. You will be introduced to Bayesian theory, with particular emphasis on conceptual foundations as well as implementation and model fitting. You will learn the essential distinctions between classical and Bayesian methods and become familiar with the origins of Bayesian inference. You will also learn about conjugate families of distributions and why they are very convenient, and how to conduct Bayesian inference with intractable posterior distributions, when you do not have conjugate distributions.

Although this course emphasizes the mathematical theory behind Bayesian inference, data analysis and interpretation of results are also important components. Students who wish to explore the mathematical theory in more detail than what is covered in class are welcome to engage with and request further reading materials from the instructor. Also, all students must have the theoretical background covered in the prerequisites to be able to keep up with and understand the materials.

Learning Objectives

By the end of this course, students should be able to

  • Understand the basics of Bayesian inference, that is, be able to define likelihood functions, prior distributions, posterior distributions, prior predictive distributions and posterior predictive distributions.
  • Derive posterior distributions, prior predictive distributions and posterior predictive distributions, for common likelihood-prior combinations of distributions.
  • Interpret the results of fitted models and conduct checks to ascertain that the models have converged.
  • Use the Bayesian methods and models covered in class to analyze real data sets.
  • Assess the adequacy of Bayesian models to any given data and make a decision on what to do in cases when certain models are not appropriate for a given data set.

Course Info

Meeting Times

  Tuesdays and Thursdays (10:15am - 11:30am)

  Zoom Meeting ID: See Sakai.


Section 01:

  Ziang Wang

  Mondays (12:00pm - 1:15pm)

  Zoom Meeting ID: See Sakai

Section 02:

  Jennifer Kampe

  Mondays (1:45pm - 3:00pm)

  Zoom Meeting ID: See Sakai

Recordings will be made available afterwards for students who are unable not to attend the live sessions.


To gain access to the pre-recorded lecture videos, you will have to create a Playposit account. There are participation quizzes embedded within the videos. These quizzes make up a part of your final grade (see: course policies) so take them seriously. To join the class on Playposit, you first need to create a new account as a student here. Next, you will use the class link, which I will send out via email, to join the class site. While you need not create an account with your Duke email, I strongly suggest you do.

Zoom meetings

The easiest way for you to join the different Zoom meetings is to log in to Sakai, go to the "Zoom meetings" tab, and click "Upcoming Meetings". For the recordings (for lab and discussion sessions), also log in to Sakai, go to the "Zoom meetings" tab, and click "Cloud Recordings". Those will be available few minutes after the sessions.

Teaching Team and Office Hours

Instructor Dr. Olanrewaju Michael Akande   Mondays: 6:00pm - 7:00pm
Thursdays: 9:00am - 10:00am
Zoom Meeting ID: See Sakai
TA Jennifer Kampe Tuesdays: 5:00pm - 6:30pm
Fridays: 5:00pm - 6:30pm
Zoom Meeting ID: See Sakai
Ziang Wang Wednesdays: 3:00pm - 5:00pm
Thursdays: 3:00pm - 4pm
Zoom Meeting ID: See Sakai


A First Course in Bayesian Statistical Methods Peter D. Hoff, 2009, New York: Springer. Required (available online from Duke library)
Bayesian Data Analysis (Third Edition) Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Optional


Lecture notes and slides, lab exercises and assigned readings will be posted on the course website, while lecture and lab videos will be posted on Sakai. White boards will also be used frequently in the lecture videos, so please pay special attention to those. Finally, we will closely follow the main textbook so students should make sure to always read the corresponding textbook chapters in the assigned readings.

Important Dates

Wed, January 20 Classes begin
Tue, February 2 Drop/Add ends
Thur, February 11 Quiz I day (tentative)
Tue - Wed, March 9 - 10 No classes held
Mon, March 15 Midterm exam day (tentative)
Thur, April 8 Quiz II day (tentative)
Mon, April 12 Wellness day
Fri, April 23 Classes end
Fri - Sat, April 30 - May 1 Final exam period

Wellness day

In lieu of a traditional class meeting on April 12, 2021, please use our regular class time to engage in reflection and wellness endeavors. A list of wellness strategies and programs is available at

Although the goal of Wellness Day 2021 is to provide time and space to engage in activities that enhance your well-being, please remember that wellness isn’t achieved in one day. Learning to balance your personal, professional, and academic commitments is a skill that should be practiced regularly and over time.

Green Classroom

This course has achieved Duke’s Green Classroom Certification. The certification indicates that the faculty member teaching this course has taken significant steps to green the delivery of this course. Your faculty member has completed a checklist indicating their common practices in areas of this course that have an environmental impact, such as paper and energy consumption. Some common practices implemented by faculty to reduce the environmental impact of their course include allowing electronic submission of assignments, providing online readings and turning off lights and electronics in the classroom when they are not in use. The eco-friendly aspects of course delivery may vary by faculty, by course and throughout the semester. Learn more at


This web page contains materials such as lecture slides, homework assignments, and datasets developed or adapted by Dr. Alexander Volfovsky, Dr. David B. Dunson and Dr. Rebecca Carter Steorts.