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While the above heading sounds like a title in reverse, its words are in the “correct” order in that our paper with George Casella, A Short History of Markov Chain Monte Carlo, has been accepted for publication by Statistical Science. This publication may sound weird when considering that the paper ... [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...]

My sabbatical is rapidly coming to an end, and I have to start thinking more and more about teaching. Glancing over my module description for the introductory computational statistics course I teach, I noticed that it’s a bit light on recommend/background reading. In fact it has only two ... [Read more...]

“The measurement outputs contain at the 99% confidence level 42 new random bits. This is a much stronger statement than passing or not passing statistical tests, which merely indicate that no obvious non-random patterns are present.” arXiv:0911.3427 As often, I bought La Recherche in the station newsagent for the wrong reason! The ... [Read more...]

Last week, I received a box of books from the International Statistical Review, for reviewing them. I thus grabbed the one whose title was most appealing to me, namely Bayesian Model Selection and Statistical Modeling by Tomohiro Ando. I am indeed interested in both the nature of testing hypotheses or ... [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...]

An interesting note was arXived a few days ago by Madeleine Thompson and Radford Neal. Beside the nice touch of mixing crumbs and slices, the neat idea is to have multiple-try proposals for simulating within a slice and to decrease the dimension of the simulation space at each try. This ... [Read more...]

The book Random effects and latent variable model selection, edited by David Dunson in 2008 as a Springer Lecture Note. contains several chapters dealing with evidence approximation in mixed effect models. (Incidentally, I would be interested in the story behind the  Lecture Note as I found no explanation in the backcover ... [Read more...]

As posted in early August from JSM 2010 in Vancouver, StatProb was launched as a way to promote an on-line encyclopedia/wiki with the scientific backup of expert reviewers. This was completely novel and I was quite excited to take part in the venture as a representative of the Royal Statistical ... [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...]

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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Stephen Stigler has written a paper in the Journal of the Royal Statistical Society Series A on Francis Galton’s analysis of (his cousin) Charles Darwin’ Origin of Species, leading to nothing less than Bayesian analysis and accept-reject algorithms! “On September 10th, 1885, Francis Galton ushered in a new era of ... [Read more...]

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...]