At JSM, John Kimmel gave me a copy of the Handbook of Markov chain Monte Carlo, as I had not (yet?!) received it. This handbook is edited by Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng, all first-class jedis of the MCMC galaxy. I had not had a chance to get a look at 
Although the official deadline is long past, I just became aware of this call for a postdoctoral position in Paris starting next Fall. Please feel free to apply: Summary Adaptive Markov Chain Monte Carlo (MCMC) methods are currently a very active field of research. MCMC methods are sampling methods, based on Markov Chains which are 
TOMACS (ACM Transactions on Modeling and Computer Simulation) is launching a call for paper submission. The special topic is Monte Carlo Methods in Statistics and Arnaud Doucet and myself are the special issue editors. Here are the details.: Over the last two decades Monte Carlo methods have attracted much attention from statisticians as they provide 
![Adap’skiii [day 2]](http://www.r-bloggers.com/wp-content/uploads/2011/01/img_7926-e1294224062331.jpg?w=450&h=264)
Another exciting day at Adap’skiii!!! Yves Atchadé presented a very recent work on the fundamental issue of estimating the asymptotic variance estimation for adaptive MCMC algorithms, with an intriguing experimental observation that a non-converging bandwidth with rate 1/n was providing better coverage than the converging rate. (I always found the issue of estimating the asymptotic 
![Adap’skiii [latest]](http://www.r-bloggers.com/wp-content/uploads/2010/12/1290618635POD_WawraCarolyn.jpg)
Just to point out there still is room for more participants to the Adap’skiii workshop! We have now reached 60 participants for this Utah workshop and would welcome more, quite obviously! All participants are also free to present a poster on the evening of the 4th, in the bar. Filed under: pictures, R, Statistics, Travel, 
![Slices and crumbs [arXiv:1011.4722]](http://l.wordpress.com/latex.php?latex=%5Cmathbf%7BJ%7D=(%5Cnabla%5Clog+f(x_1),%5Cldots,%5Cnabla%5Clog+f(x_k))&bg=000000&%23038;fg=B0B0B0&%23038;s=0)
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 dimension diminution is achieved via 
![Robust adaptive Metropolis algorithm [arXiv:10114381]](http://www.r-bloggers.com/wp-content/uploads/2010/11/am-adapt-student.jpg?w=102&h=117)
Matti Vihola has posted a new paper on arXiv about adaptive (random walk) Metropolis-Hastings algorithms. The update in the (lower diagonal) scale matrix is where is the current acceptance probability and the target acceptance rate; is the current random noise for the proposal, ; is a step size sequence decaying to zero. The spirit of 
![Graphical comparison of MCMC performance [arXiv:1011.445]](http://www.r-bloggers.com/wp-content/uploads/2010/11/fig-cpu.jpg?w=200&h=269)
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 represent distributions and columns represent 
We have just posted the (mostly definitive) program for Adap’skii, January 3-4, The Canyons, Utah. This is taking place just before and as a satellite of the larger MCMSki III conference, January 4-7, same location. The registration for the conference and for lodging is available through the MCMCSki III registration page, Remember also that this 
Glancing perchance at the back of my Amstat News, I was intrigued by the SAS advertisement Bayesian Methods Specify Bayesian analysis for ANOVA, logistic regression, Poisson regression, accelerated failure time models and Cox regression through the GENMOD, LIFEREG and PHREG procedures. Analyze a wider variety of models with the MCMC procedure, a general purpose Bayesian 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
