This post is somewhat marginal to R in that there are several statistical systems that could be used to tackle the problem. Bayesian statistics is one of those topics that I would like to understand better, much better, in fact. Unfortunately, I struggle to get the time to attend courses on the topic between running my own lectures, research and travel; there are always books, of course.

After we had some strong earthquakes in Christchurch we have had limited access to most part of our *physical* library (still had full access to all our electronic collection). Last week I had a quick visit to the library and picked up three introductory books: Albert’s Bayesian computation with R, Marin and Robert’s Bayesian core: a practical approach to computational Bayesian statistics and Bolstad’s Understanding computational Bayesian statistics (all links to LibraryThing). My intention was to see if I could use one (or several of them) to start on the topic. What follows are my (probably unfair) comments after reading the first couple of chapters of each book.

In my (highly individual and dubious) opinion Albert’s book is the easiest to read. I was waiting to see the doctor while reading—and actually understanding—some of the concepts. The book is certainly geared towards R users and gradually develops the code necessary to run simple analyses from estimating a proportion to fitting (simple) hierarchical linear models. I’m still reading, which is a compliment.

Marin and Robert’s book is quite different in that uses R as a vehicle (like this blog) but the focus is more on the conceptual side and covers more types of models than Albert’s book. I do not have the probability background for this course (or maybe I did, but it was ages ago); however, the book makes me want to learn/refresh that background. An annoying comment on the book is that it is “self-contained”; well, anything is self-contained if one asks for enough prerequisites! I’m still reading (jumping between Albert’s and this book), and the book has managed to capture my interest.

Finally, Bolstad’s book. How to put this? “It is not you, it is me”. It is much more technical and I do not have the time, nor the patience, to wait until chapter 8 to do something useful (logistic regression). This is going back to the library until an indeterminate future.

If you are now writing a book on the topic I would like to think of the following user case:

- the reader has little or no exposure to Bayesian statistics, but it has been working for a while with ‘classical’ methods,
- the reader is self-motivated, but he doesn’t want to spend ages to be able to fit even a simple linear regression,
- the reader has little background on probability theory, but he is willing to learn some in between learning the tools and to run some analyses,
- using a statistical system that allows for both classical and Bayesian approaches is a plus.

It is hard for me to be more selfish in this description; you are potentially writing a book for me.

**P.S.** After publishing this post I remembered that I came across a PDF copy of Doing Bayesian Data Analysis: A Tutorial with R and BUGS by Kruschke. Setting aside the dodginess of the copy, the book looked well-written, started from first principles and had puppies on the cover (!), so I ordered it from Amazon.

**P.D. 2011-12-03 23:45 AEST** Christian Robert sent me a nice email and wrote a few words on my post. Yes, I’m still plodding along with the book although I’m taking a ten day break while traveling in Australia.

**P.D. 2011-11-25 12:25 NZST** Here is a list of links to LibraryThing for the books suggested in the comments:

- Scott Lynch. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists.
- Peter Hoffe. A First Course in Bayesian Statistical Methods.
- Andrew Gelman and Jennifer Hill. Data Analysis Using Regression and Multilevel/Hierarchical Models.
- Joseph Kadane. Principles of Uncertainty. Way too complex for what the post is calling for, but free PDF available.