Stat 6103, Bayesian Data Analysis

Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss the relevant theory and get to open questions in model building, computing, evaluation, and expansion. The course is intended for students who want to do applied statistics and also those who are interested in working on statistics research problems.

Stat 8307, Statistical Communication and Graphics

Communication is central to your job as a quantitative researcher. Our goal in this course is for you to improve at all aspects of statistical communication, including writing, public speaking, teaching, informal conversation and collaboration, programming, and graphics. With weekly assignments and group projects, this course offers you a chance to get practice and feedback on a range of communication skills. All this is in the context of statistics; in particular we will discuss the challenges of visualizing uncertainty and variation, and the ways in which a deeper integration of these concepts into statistical practice could help resolve the current statistical crisis in science. Statistics research is not separate from communication; the two are intertwined, and this course is about you putting in the work to become a better writer, teacher, speaker, and statistics practitioner.

The communication and graphics course should be no problem; I’ll teach it pretty much how I taught it last year, with 2 meetings a week, diaries, jitts, homeworks, class discussions, projects, etc.

The Bayes class I’ll be doing in a new way. It’ll meet once a week, and my plan is for the first half of each class to be a discussion of material from the book and in the second half for students to work together using Stan, with me and the teaching assistant walking around helping. Also, the homeworks will be more Stan-centered. The idea is for the students to really learn applied Bayesian statistics, as well as to have a chance to grapple with important theoretical concepts and to be introduced to the research frontier. We’ll see how it goes. The key will be coming up with in-class and homework assignments that give students the chance to fit Bayesian models for interesting problems.