Bayesian methods are increasingly important in both industry and academia. This is a graduate-level course, within the Department of Statistical Science, 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 outside of class. Also, all students must have the theoretical background covered in the prerequisites to be able to keep up with and understand the materials.
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.
Updates Effective March 23
As most (and hopefully all) of you know by now, classes will be moving online, following Duke University directives, given the current situation with respect to COVID-19. Consequently, I have highlighted the most important changes to the syllabus (and more generally, the website) in red, so please pay special attention to those.
Old Chemistry 116 Zoom Meeting ID: 359-482-959 (URL: https://duke.zoom.us/j/359482959)
Wed/Fri 11:45am - 01:00pm
Sociology Psychology 127 Zoom Meeting ID: 230-936-664 (URL: https://duke.zoom.us/j/230936664)
Mon 11:45am - 01:00pm
Old Chemistry 101 Zoom Meeting ID: 520-899-963 (URL: https://duke.zoom.us/j/520899963)
Mon 01:25pm - 02:40pm
Teaching Team and Office Hours
|Instructor||Dr. Olanrewaju Michael Akande||Wed 9:00 - 10:00am; Thur 11:45 - 12:45pm|
|Lead TA||Jordan Bryan (mainly for STA 360)|
|TAs||Zhuoqun (Carol) Wang||Tues 3:00 - 5:00pm|
|Bai Li||Wed 3:00 - 5:00pm|
|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 other reading resources will be posted on the course website. In-class black/white boards will also be used frequently so class attendance in required. Finally, we will closely follow the main textbook so students should make sure to always read the corresponding textbook chapters per topic, outside of class.
|Fri, January 10||First class for STA 602L (not January 8!!!)|
|Mon, January 20||Martin Luther King Jr. Day; no classes!|
|Wed, January 22||Drop/add ends|
|Wed, February 12||Quiz I|
|Fri, March 6||Midterm exam;
Spring break begins 7:00pm
|Mon, March 23||Spring break ends; classes resume 8:30am|
|Wed, April 15||Graduate classes end|
|Sat, May 2||Final exam|
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 https://sustainability.duke.edu/action/certification/classroom.