Last week I was hosted by Mike Palopoli and the Bowdoin College Biology Department, where I gave a departmental seminar on my current work on Bayesian divergence time estimation methods. Bowdoin College is a 4-year liberal arts college with some very bright undergraduates. Several of the honors biology majors attended my talk and after the seminar Mike and I led a discussion of my work and computational evolutionary biology, in general.
During this discussion I got stumped by a question from one of the students. He asked if I could recommend a basic and gentle primer on Bayesian statistics for someone with very little statistics training. (This particular student is currently involved in a population genetics project using Bayesian analysis.) I recommended online teaching materials from the various phylogenetics/molecular evolution workshops, including Bodega and this blog, but I couldn’t point him toward a book that I felt would be suitable for someone without a strong understanding of probability theory. As a graduate student, I took Bill Jefferys’ Bayesian Inference course at the University of Texas, where we used Bayesian Data Analysis by Gelman et al. This book is a great introduction (as was Bill’s awesome course), but might be too advanced even for a bright and motivated undergraduate biology major.
So my goal for my first post on the Treethinkers’ blog is to seek out suggestions from readers: What is a good, introductory primer on Bayesian inference that is suitable for the undergraduate level?
Please add your suggestions in the comments below.