Andrew Gelman has just written a most entertaining review of “Introducing Monte Carlo Methods with R” on his blog. The first sentence is ominous as the book seemingly reminded him of communists and fascists…! The explanation for this frightening debut is that the connection between the components of statistics
… ↔ Probability theory ↔ Theoretical statistics↔Statistical methodology ↔ Applications ↔ Computation ↔ Probability theory ↔ …
may be seen as a torus just as the range of political ideologies, the argument being that both George and I switched from proving mathematical minimaxity theorems about James-Stein estimators to proving convergence theorems. about Metropolis-Hastings algorithms. After pondering Andrew’s lines for a while, I am far from sure this is a positive assessment of Introducing Monte Carlo Methods with R! Indeed, at the first glance, it may give the blog reader the feeling that this is yet another theoretical book about Monte Carlo methods, written by theorists and mainly for theorists (Andrew wrote “applied researchers such as myself will get much more use out of theory as applied to computation“)… While we strive to distance ourselves from making a baby version of Monte Carlo Statistical Method, choosing the format of a Use R! book to clarify even further the purpose of the book: to lead (our students and) our readers to understand Monte Carlo methods through worked-out examples to the point of developping their own methods, while keeping the theory at bay.
A second read shows that Andrew’s point is much more subtle, namely that as (formerly?) mathematical statisticians, we have adopted a terse style that (maybe unconsciously) shy way from giving too much detail and explanations: once a definition is provided, it should suffice to itself! This leads to what Andrew calls little puzzles, where the reader needs to stop and reason out why things are as they are. (“I noticed a bunch of other examples of this sort, where the narrative just flows by and, as a reader, you have to stop and grab it. Lots of fun.”) I noticed the same reactions from my students, so I quite agree with this point. When learning with a book, you need to sit with a piece of paper on one side (if the margins are too narrow), your computer on the other side and test everything for yourself. This is actually an intended feature, if not spelled out more clearly, and I thus appreciate very much Andrew’s conclusion that “it would also be an excellent book for a course on statistical computing“!
There is also Andrew’s comment that the book is ugly, which stings, but again can be seen in a different light.I obviously do not find Introducing Monte Carlo Methods with R ugly but the printing could have been indeed nicer and the fact that the printers used the jpeg versions of the figures instead of the postscript or pdf versions did not help. The raw R output presented verbatim in most pages is not particularly beautiful either, but this is truly intended, for readers who cannot test the code immediately (as when reading in the metro or listening to the course at the same time). The R programs are far from perfect R programs, but examples of what a “standard” beginner would do. I also agree with the suggestion of an epilogue: we wrote several times during the course of the book that we were not providing the big picture and that many aspects of the Monte Carlo methodology were not covered, but this would be worth repeating at the end, along with the few general recommendations we can make about better R programming. Another thing to add in the next edition!
A final interesting remark is that the very first comment on Andrew’s post was about solutions! This is a strong request from readers. nowadays, and thus seems like a compulsory element of publishing books with exercises. (As we discovered a wee too late for Bayesian Core!)