I was grading my Master projects this morning and came upon this graph: which compares the variability of an importance-sampling estimator versus its self-normalised alternative… This is an interesting case in that self-normalisation does considerably degrade the quality of the approximation in that setting. In other cases, self-normalisation may bring a clear improvement. (This reminded 
An X’idated reader of Monte Carlo Statistical Methods had trouble with our Example 3.13, the very one our academic book reviewer disliked so much as to “diverse a 2 star”. The issue is with computing the integral when f is the Student’s t(5) distribution density. In our book, we compare a few importance sampling solutions, 
I just got the following email from PNAS about our Lack of confidence in ABC model choice. Editor's Remarks to Author: both referees now find the manuscript acceptable for publication as do I. Each suggests small changes which I encourage the authors to make prior to having the manuscript go into production. Congratulations on an 
![Lack of confidence [revised]](http://feeds.wordpress.com/1.0/comments/xianblog.wordpress.com/10127/)
Following the comments on our earlier submission to PNAS, we have written (and re-arXived) a revised version where we try to spell out (better) the distinction between ABC point (and confidence) estimation and ABC model choice, namely that the problem was at another level for Bayesian model choice (using posterior probabilities). When doing point estimation 
Martin Kilbinger, an astronomer (cosmologist) with whom we had worked on population Monte Carlo for cosmological inference , has made the PMC C codes available on the CosmoPMC webpage. He has also written a CosmoPMC manual that is now available from arXiv. And he very kindly associated me to 
With Pierre Jacob, my PhD student, and Murray Smith, from National Institute of Water and Atmospheric Research, Wellington, who actually started us on this project at the last and latest Valencia meeting, we have completed a paper on using parallel computing in independent Metropolis-Hastings algorithms. The paper is arXived and the abstract goes as follows: 
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 in Introducing Monte 
Today and tomorrow, I am attending the annual Italian statistical society meeting. While I appreciate very much the invitation, as well as the opportunity to walk through Padova and Venezia for a short (and alas rainy!) hour on the way there (leaving home at 8am, walking in Venezia at noon!), I am rather skeptical of 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
