We agree that more R code could have been included in the book and we contemplate including most R codes in the second edition of the book. Our reluctance to do so in the first edition was due to the fact that, as the complexity of the topic increases, so does the length of the R code and thus the difficulty of commenting it. |
The book was designed as a textbook for undergraduate and/or graduate students and therefore it somehow rules out self-study except for most advanced or mature students. This is also the reason why solutions to the exercises are not provided, except for instructors on Springer's website. When CPR taught from the book in New-Zealand, the third year [math & stat) students who took the course managed to solve the exercises despite a limited probabilistic background. The reference to the Lebesgue measure in Exercise 1.1 is akward and unnecessary, and it should vanish from the second edition. Same thing for Exercise 2.1. A good knowledge of Riemann integration is however necessary to handle Bayesian computations: this is unavoidable. So we agree that some prior exposure to probability theory and to mathematical statistics is appropriate, even though a complete coverage of Casella and Berger (2001) is not necessary. |
The motivation for the topics was to get into the major aspects of Bayesian Statistics through datasets (and models) that would reflect the variety of the applications of Bayesian Statistics. While capture-recapture may appear as an over-specialised topic (and this was also stressed in earlier reviews), we think it is a good motivating entry to (a) ecological data, (b) discrete sampling models, (c) longitudinal data, (d) missing variables, and (e) hidden Markov models. The very beginning of Chapter 5 deals with the simple binomial model, while Chapter 4 ends up with contingency tables. Obviously, there will always be important models that are not covered in a 246 page book and we had no intention to be exhaustive. The second edition may include one or two more chapters, maybe covering meta-analysis and hierachical models, but even such an addition cannot fully answer the criticism... |
The decision-theoretic motivations for using Bayesian Statistics are obviously essential and this is the theme underlying The Bayesian Choice. However, this book is primarily intended for (future) practical implementations of Bayesian Statistics, and the use of loss functions is marginal in most studies. With more space, we could address realistic loss problems, including Bayesian design or multiple comparisons, but we had to make choices. The comment on BIC is intended for our students, who may have encountered BIC as a black-box alternative to AIC, not for specialists of the field, and we think that exposing the connection would have taken too much time, while being (a) too advanced for most students and (b) not completely convincing. Note that we personally disagree with the use of BIC in a Bayesian framework (see the discussion in The Bayesian Choice, second edition). |
There are indeed many typos, for which we apologise, and we are very grateful to all readers, including the reviewer, for pointing them out. A first batch [listed on the webpage] was corrected for the second printing of the first edition and we are now keeping track [see webpage] of additional typos for the third printing. |
Thanks for the suggestions: we will indeed increase the coverage of R in the second edition, without hopefully introducing new typos! For the reason mentioned above, we cannot make solutions to exercises available for students. And we completely agree that the book requires a good instructor to be taught from, hoping that the availability of slides, R codes, and latex files will help those teaching from the book. The comparison with Gelman et al. (2003) and Tanner (2002) is delicate to discuss, but we feel that Bayesian Core provides a more realistic understanding of Bayesian data analysis, thanks to the constant reference to a supporting dataset and to a more directive discussion on the choice of prior distributions. |