I spent most of the Sunday afternoon trying to understand why defining did not have the same effect as writing the line until I found there is a clash due to the comment package… The assuredly simple code produces an error message: This is quite an inconvenience as I need ... [Read more...]

I received the following email about Introducing Monte Carlo Methods with R a few days ago: Hallo Dr. Robert, I  am studying your fine book for myself. There´s a little problem in examples 7.17 and 8.1: in the R code a function “gu” is used and a reference given to ex. 5.17, ... [Read more...]

The two translators of our book in Japanese, Kazue & Motohiro Ishida, contacted me about some R code mistakes in the book. The translation is nearly done and they checked every piece of code in the book, an endeavour for which I am very grateful! Here are the two issues they ... [Read more...]

Jean-Michel (aka Jean-Claude!) Marin came for a few days so that we could make late progress on the revision of our book Bayesian Core towards an Use R! version. In one of the R programs in the mixture chapter, we were getting improbable answers, until we found an R mistake ... [Read more...]

Boris from Ottawa sent me this email about Introducing Monte Carlo Methods with R: As I went through the exercises and examples, I believe I found a typo in exercise 6.4 on page 176 that is not in the list of typos posted on  your website.  For simulation of Gamma(a,1) random ... [Read more...]

The March 2011 issue of JASA contains a review of Introducing Monte Carlo Methods with R by Hedibert Lopes. As in the previous review, the poor quality of the figures is (rightly) pointed out by Hedie. However, the main message of the review remains very positive and, furthermore, Hedie advertises the ‘... [Read more...]

After posting so many entries about typos in my books (making you wonder how there could be any text left!) and postponing their classification for so long, I decided on Saturday afternoon to collect those entries into a comprehensive pdf document that should be more useful for readers. I incidentally ... [Read more...]

A casualty of cut-and-paste in Chapter 3 of Introducing Monte Carlo Methods with R. Brad McNeney from Simon Fraser sent me a nice email about the end of Example 3.6 missing a marginal estimate. Indeed, it does. And it should have been obvious from the “estimates” we derived, 19 and 16, which are not ... [Read more...]

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Ashley put the following comment on Chapter 5 of Introducing Monte Carlo Methods with R”: I am reading chapter 5. I try to reproduced the result on page 128. The R codes don’t work on my laptop. When I try to run the following codes on page 128 __ for (i in 1:(nlm(like,... [Read more...]

The translation of the book Introducing Monte Carlo Methods with R is close to being completed. The copy-editing and page-setting are done, I have received the cover proposal and am happy with it, so it should now go to production and be ready by early January, (earlier than the tentative ... [Read more...]

A new posting on arXiv by Madeleine Thompson on a graphical tool for assessing performance. She has developed a software called SamplerCompare, implemented in R and C. The graphical evaluation plots “log density evaluations per iteration times autocorrelation time against a tuning parameter in a grid of plots where rows ... [Read more...]

The class started yesterday with a small but focussed and responsive audience! Given the background of the students, and in particular their clear proficiency in R!, I switched between the original slides of Introducing Monte Carlo Methods with R and those of my Monte Carlo Statistical Methods: course, updated by ... [Read more...]

As already announced on Statisfaction, I will start a short [14 hour] course in English based on Introducing Monte Carlo Methods with R at ENSAE next Tuesday. The slides were written by George Casella for a course he gave in Italy last spring and he kindly agreed on making them available ... [Read more...]

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In the previous days I have received several emails asking for clarification of the effective sample size derivation in “Introducing Monte Carlo Methods with R” (Section 4.4, pp. 98-100). Formula (4.3) gives the Monte Carlo estimate of the variance of a self-normalised importance sampling estimator (note the change from the original version ... [Read more...]

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Edward Kao pointed out the following difficulty about Example 3.6 in Chapter 3 of “Introducing Monte Carlo Methods with R”: I have two questions that have puzzled me for a while. I hope you can shed some lights. They are all about Example 3.6 of your book. 1. On page 74, there is a term [...] [Read more...]