Martin Weinberg posted on arXiv a revision of his paper, Computing the Bayesian Factor from a Markov chain Monte Carlo Simulation of the Posterior Distribution, that is submitted to Bayesian Analysis. I have already mentioned this paper in a previous post, but I remain unconvinced of the appeal of the paper method, given that it 
Sample once from the Uniform(0,1) distribution. Call the resulting value . Multiply this result by some constant . Repeat the process, this time sampling from Uniform(0, ). What happens when the multiplier is 2? How big does the multiplier have to be to force divergence. Try it and see: iters = 200 locations = rep(0,iters)
I must confess to feeling an almost obsessive fascination with intransitive games, dice, and other artifacts. The most famous intransitive game is rock, scissors, paper. Rock beats scissors. Scissors beats paper. Paper beats rock. Everyone older than 7 seems to know this, but very few people are aware that dice can exhibit this same behavior,
The standard, textbook way to represent a density function looks like this: Perhaps you have seen this before? (Plot created in R, all source code from this post is included at the end). Not only will you find this plot in statistics books, you’ll also see it in medical texts, sociology, and even economics books.
R did definitely not start to be THE statistical computing tool. The “two Rs” in far down-under just needed some tool which was not too expensive and structured enough to support the elementary statistics classes filled with hundreds of students. Another constraint was the computing lab which was large enough, but “only” filled with Mac
In connection with the Valencia 9 meeting that started yesterday, and with Hedie‘s talk there, we have posted on arXiv a set of comments on particle learning. The arXiv paper contains several discussions but they mostly focus on the inevitable degeneracy that accompanies particle systems. When Lopes et al. state that is not of interest 
There are many reasons to create an R package, such as codes protection, convenient usage, etc. However, creating an R package in Unix is not hard, it IS in Windows, as R is designed in a Unix environment which includes a set of compilers, programming utilities, and text-formatting routines while Windows lacks those. I used to build an R...
![Vanilla Rao-Blackwellisation [re]revised](http://l.wordpress.com/latex.php?latex=%5Cleft%7C+%5Cbegin%7Bmatrix%7D+%5Ctau+%26%5Ctext%7Bmedian%7D+%26%5Ctext%7Bmean+%7D%26q_%7B.8%7D+%26q_%7B.9%7D+%26%5Ctext%7Btime%7D%5C%5C+0.25+%260.0+%268.85+%264.9+%2613+%264.2%5C%5C+0.50+%260.0+%266.76+%264+%2611+%262.25%5C%5C+1.00+%260.25+%266.15+%264+%2610+%262.5%5C%5C+2.00+%260.20+%265.90+%263.5+%268.5+%264.5%5C%5C%5Cend%7Bmatrix%7D+%5Cright%7C&bg=000000&fg=B0B0B0&s=0)
Although the revision is quite minor, it took us two months to complete from the time I received the news in the Atlanta airport lounge… The vanilla Rao-Blackwellisation paper with Randal Douc has thus been resubmitted to the Annals of Statistics. And rearXived. The only significant change is the inclusion of two tables detailing computing 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
