I have at last managed to get over this hard chapter of the R book with George Casella, namely the one on stochastic optimisation techniques mentioned in the earlier post, so we have now reached five completed chapters for the first draft,
- Introduction to R programming
- Random variable generation
- Monte Carlo methods
- Controlling and accelerating convergence
- Monte Carlo optimization
with 168 pages and 51K words, not to mention 46 figures… I am thus two weeks behind my schedule and I should soon attack the chapter on MCMC convergence assessment which is somehow straightforward, even though the version presented in Chapter 11 of Monte Carlo Statistical Methods needs a huge revamping. Given the curent size, it is very unlikely we will stick to the 200 pages goal! Especially when the two remaining chapters are about Metropolis algorithms and Gibbs sampling. But I am fairly confident to get the final draft ready by early March.
Ps-Following the very critical review discussed in this post, two more balanced reviews of Bayesian Core have now appeared on amazon.com, including one by the marathon reviewer Michael Chernick. His criticisms are well taken: the book is a bit advanced for most undergraduates and practionners, while the R part is not developped enough, compared with Jim Albert’s book. The other reviewer, A Dent (as in to make a dent?), also has a globaly positive perspective while asking for more examples and solutions to the exercises, a difficulty discussed in the earlier post.