After a sunny weekend to unpack and unwind, I am now back to my normal schedule, on my way to Paris-Dauphine for an R (second-chance) exam. Except for confusing my turn signal for my wiper, thanks to two weeks of intensive driving in four Australian states!, things are thus back to “normal”, meaning that I 
+=+%5Cmathbb%7BP%7D(%5Csup_%7Bs%5Cle+t%7DX_s+%5Cge+c)+%5Cle+1/c&bg=000000&fg=B0B0B0&s=0)
A surprising paper came out in the last issue of Statistical Science, linking martingales and Bayes factors. In the historical part, the authors (Shafer, Shen, Vereshchagin and Vovk) recall that martingales were popularised by Martin-Löf, who is also influential in the theory of algorithmic randomness. A property of test martingales (i.e., martingales that are non 
On Friday, a 2008 paper on Stochastic Approximation and Newton’s Estimate of a Mixing Distribution by Ryan Martin and J.K. Ghosh was posted on arXiv. (I do not really see why it took so long to post on arXiv a 2008 Statistical Science paper but given that it is not available on project Euclid, it 
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 is also scheduled to appear 
![Particle learning [rejoinder]](http://www.r-bloggers.com/wp-content/uploads/2010/11/bpl1c5no-n10000.jpg?w=360&h=360)
Following the posting on arXiv of the Statistical Science paper of Carvalho et al., and the publication by the same authors in Bayesian Analysis of Particle Learning for general mixtures I noticed on Hedibert Lopes’ website his rejoinder to the discussion of his Valencia 9 paper has been posted. Since the discussion involved several points 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
