How to become a Bayesian in eight easy steps: An annotated reading list
(TLDR: We wrote an annotated reading list to get you started in learning Bayesian statistics. Here is the paper.)
It can be hard to know where to start when you want to learn about Bayesian statistics. I am frequently asked to share my favorite introductory resources to Bayesian statistics, and my go-to answer has been to share a dropbox folder with a bunch of PDFs that aren’t really sorted or cohesive. In some sense I was acting as little more than a glorified Google Scholar search bar.
It seems like there is some tension out there with regard to Bayes, in that many people want to know more about it, but when they pick up, say, Andrew Gelman and colleagues’ Bayesian Data Analysis they get totally overwhelmed. And then they just think, “Screw this esoteric B.S.” and give up because it doesn’t seem like it is worth their time or effort.
I think this happens a lot. Introductory Bayesian texts usually assume a level of training in mathematical statistics that most researchers simply don’t have time (or otherwise don’t need) to learn. There are actually a lot of accessible Bayesian resources out there that don’t require much math stat background at all, but it just so happens that they are not consolidated anywhere so people don’t necessarily know about them.
Enter the eight step program
Beth Baribault, Peter Edelsbrunner (@peter1328), Fabian Dablander (@fdabl), Quentin Gronau, and I have just finished a new paper that tries to remedy this situation, titled, “How to become a Bayesian in eight easy steps: An annotated reading list.” We were invited to submit this paper for a special issue on Bayesian statistics for Psychonomic Bulletin and Review. Each paper in the special issue addresses a specific question we often hear about Bayesian statistics, and ours was the following:
I am a reviewer/editor handling a manuscript that uses Bayesian methods; which articles should I read to get a quick idea of what that means?
So the paper‘s goal is not so much to teach readers how to actually perform Bayesian data analysis — there are other papers in the special issue for that — but to facilitate readers in their quest to understand basic Bayesian concepts. We think it will serve as a nice introductory reading list for any interested researcher.
The format of the paper is straightforward. We highlight eight papers that had a big impact on our own understanding of Bayesian statistics, as well as short descriptions of an additional 28 resources in the Further reading appendix. The first four papers are focused on theoretical introductions, and the second four have a slightly more applied focus.
We also give every resource a ranking from 1–9 on two dimensions: Focus (theoretical vs. applied) and Difficulty (easy vs. hard). We tried to provide a wide range of resources, from easy applications (#14: Wagenmakers, Lee, and Morey’s “Bayesian benefits for the pragmatic researcher”) to challenging theoretical discussions (#12: Edwards, Lindman and Savage’s “Bayesian statistical inference for psychological research”) and others in between.
The figure below (Figure A1, available on the last page of the paper) summarizes our rankings:
The emboldened numbers (1–8) are the papers that we’ve commented on in detail, numbers in light text (9–30) are papers we briefly describe in the appendix, and the italicized numbers (31–36) are our recommended introductory books (also listed in the appendix).
This is how we chose to frame the paper,
Overall, the guide is designed such that a researcher might be able to read all eight of the highlighted articles and some supplemental readings within a few days. After readers acquaint themselves with these sources, they should be well-equipped both to interpret existing research and to evaluate new research that relies on Bayesian methods.
Here’s the list of papers we chose to cover in detail:
- Lindley (1993): The analysis of experimental data: The appreciation of tea and wine. PDF.
- Kruschke (2015, chapter 2): Introduction: Credibility, models, and parameters. Available on the DBDA website.
- Dienes (2011): Bayesian versus orthodox statistics: Which side are you on? PDF.
- Rouder, Speckman, Sun, Morey, & Iverson (2009): Bayesian t tests for accepting and rejecting the null hypothesis. PDF.
- Vandekerckhove, Matzke, & Wagenmakers (2014): Model comparison and the principle of parsimony. PDF.
- van de Schoot, Kaplan, Denissen, Asendorpf, Neyer, & Aken (2014): A gentle introduction to Bayesian analysis: Applications to developmental research. PDF.
- Lee and Vanpaemel (from the same special issue): Determining priors for cognitive models. PDF.
- Lee (2008): Three case studies in the Bayesian analysis of cognitive models. PDF.
You’ll have to check out the paper to see our commentary and to find out what other articles we included in the Further reading appendix. We provide urls (web archived when possible; archive.org/web/) to PDFs of the eight main papers (except #2, that’s on the DBDA website), and wherever possible for the rest of the resources (some did not have free copies online; see the References).
I thought this was a fun paper to write, and if you think you might want to learn some Bayesian basics I hope you will consider reading it.
Oh, and I should mention that we wrote the whole paper collaboratively on Overleaf.com. It is a great site that makes it easy to get started using LaTeX, and I highly recommend trying it out.
This is the fifth post in the Understanding Bayes series. Until next time,