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Bayesian Statistics

Welcome to the course website for my (Patrick E. McKnight) course: Psychology (PSYC) 757 - An Introduction to Bayesian Statistics. The course focuses on just that - introducing psychology graduate students to Bayesian statistics. The course was designed from previous incarnations to increase student involvement and reduce the workload to a manageable level. Learning all the relevant pieces to the topic can be daunting to most graduate students. I try to make this a gentle process that involves group work and shared workloads throughout the semester. Further, after more than 20 years teaching graduate students, I learned that heaping tons of reading, programming, and new concepts into 15 weeks is a fool's errand. Students simply do not do the work and, should we adopt Draconian measures to fail students who do not "rise to the occasion, " we end up failing ourselves and our field. My goal with this class is to provide every student the opportunity to implement basic Bayesian methods to standard social/behavioral science data analysis problems.

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History of the course: In previous versions, I attempted to implement a standard lecture (2010), problem-focused (2013), and flipped-learning (2016), and project-based learning (2019) formats - each offered some advantages to students but none satisfied my goal. This year (2020), I plan to implement a group, project-based approach with a blend of flipped-learning, lecture, and problem-focused pieces. With COVID, we met online instead of in-person; that change did not really affect my course or the format I chose but we lost our personal connection. For 2022, as we exit the pandemic (I hope), I chose to meet in-person and shift to a more collaborative learning environment. January 2024 brings with it a breath of fresh air. I have a newfound passion to break the content into a two semester course. Now, I give the students a choice - material suitable for a gentle introduction (coding light) or a deeper dive (coding moderate).

Purpose: The purpose of the class remains focused on Bayesian statistics, however, unlike many Bayesian devotees, I consider Bayesian statistics a tool. Just like other tools, Bayesian methods have suitable and unsuitable applications. Students enrolled in my course may find my approach more challenging than standard graduate lectures because I emphasize mastery (i.e., learning) over achievement (i.e., grades). I demand more from you (and me) so that we all learn together. If this format is more difficult, why would I implement it? Simple. I want students to learn, not just get grades for taking a class. The more my students learn, the more future professional opportunities await them.

Website: I designed the following pages to make the course more organized and accessible to all students - past, present, and future. My aim here is to enable students to study ahead, revisit material, and consult informative resources while learning Bayesian statistics. Please do not hesitate to submit comments about the site or any linked materials - comments may be submitted to me directly via email or via the google group (subscription needed to post). Thank you for visiting the site and welcome to the course.

Relevant Links:

2022 Syllabus (Previous Years' Version of the Syllabus)
Please be sure to monitor the Google Group forum for any updates. I subscribed everyone who remained registered after the first week.
The course is supported, in part, by the good folks at Posit. Through their generosity, we are able to use Posit Connect to share materials relevant to the course. I think you will find the connection via Posit to be much more efficient than standard file sharing methods (e.g., Google Drive or GitHub).

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