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 in the shape of which 
![The foundations of Statistics [reply]](http://images.springer.com/cda/content/image/cda_displayimage.jpg?SGWID=0-0-16-814797-0)
Shravan Vasishth has written a response to my review both published on the Statistics Forum. His response is quite straightforward and honest. In particular, he acknowledges not being a statistician and that he “should spend more time studying statistics”. I also understand the authors’ frustration at trying “to recruit several statisticians (at different points) to 
“We have seen that a perfect correlation is perfectly linear, so an imperfect correlation will be `imperfectly linear’.” page 128 This book has been written by two linguists, Shravan Vasishth and Michael Broe, in order to teach statistics “in areas that are traditionally not mathematically demanding” at a deeper level than traditional textbooks “without using 
The R Programming wikibook is an open source community project that "aims to create a cross-disciplinary practical guide to the R programming language." It was launched in June 2011 and is seeking content and contributors. The full call for the R Progr...
Here is an interesting question from Tomàs that echoes a lot of related emails: I’m turning to you for advice. I’m facing problem where parameter space is bounded, e.g. all parameters have to be positive. If in MCMC as proposal distribution I use normal distribution, then at some iterations I get negative proposals. So my 
Following a link on R-bloggers, I ended up on this page (with a completely useless graph that only contained the pieces of information 5% in 1900 and 55% in 2000). The author (Ralph Keeney) reports on “A remarkable 55 percent of deaths for people age 15 to 64 can be attributed to decisions with readily 
Through the webpage of the Advanced Monte Carlo Methods I & II, given a few years ago by Michael Mascagni at ETH Zürich, I found a link to the scanned version of the 1964 book Monte Carlo Methods by Hammersley and Handscomb. This is a short book, with less than 150 pages, especially if one 
“We don’t need likelihood functions; we just need to know how to simulate from (…) We don’t need models with sufficient statistics; we just need summary statistics (…) We don’t need to be Bayesian; we just need to be approximately so. We don’t need theory to tell us our method works; we just need 
![A survey of the [60′s] Monte Carlo methods [2]](http://xianblog.files.wordpress.com/2011/05/img_8168-e1305488888661.jpg?w=315&h=344)
The 24 questions asked by John Halton in the conclusion of his 1970 survey are Can we obtain a theory of convergence for random variables taking values in Fréchet spaces? Can the study of Monte Carlo estimates in separable Fréchet spaces give a theory of global approximation? When sampling functions, what constitutes a representative sample 
![A survey of [the 60's] Monte Carlo methods](http://xianblog.files.wordpress.com/2011/05/img_8170.jpg?w=450&h=337)
“The only good Monte Carlos are the dead Monte Carlos” (Trotter and Tukey, quoted by Halton) When I presented my history of MCM methods in Bristol two months ago, at the Julian Besag memorial, Christophe Andrieu mentioned a 1970 SIAM survey by John Halton on A retrospective and prospective survey of the Monte Carlo 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
