When looking around on Amazon, I found that “Introducing Monte Carlo Methods with R” was associated with another very recently published (same day as ours!) book, “Understanding Computational Bayesian Statistics“, by William Bolstad, that seems to mostly cover the same ground as us (with some connections with Bayesian Core for prior modelling in regression and logistic models). Although R seems to be less proeminently advocated than in our Use R! volume, I am quite curious to see what exactly is in this book and how much of a competitor it is! (Given that it is the same length as ours (about 315 pages), I am however a bit surprised at the high $110’s asked for this book.)
Archive for Monte Carlo
Already a competitor?!
Posted in Books, R, Statistics with tags Bayesian computation, Bayesian statistics, Gibbs sampling, MCMC, Monte Carlo, R, simulation on December 12, 2009 by xi'anIntroduction à Monte Carlo en R
Posted in Books, R, Statistics with tags Book, George Casella, Introducing Monte Carlo Methods with R, Monte Carlo, R, simulation, Springer-Verlag, translation on November 12, 2009 by xi'an
Following a proposal by Springer-Verlag Paris, I have decided to translate Introducing Monte Carlo Methods with R with George Casella into French, since a new collection of R books (in French) is planed for the Spring of 2010. The translation will a priori be done by Joachim Robert and Robin Ryder, under my supervision and with the support of Springer-Verlag Paris. I have already translated the first chapter as I needed to cut most of the R coverage, since this collection assumes a prior knowledge of R and aims at a smaller number of pages (around 200) to keep the price as low as possible.
“Introducing Monte Carlo Methods with R” published for X’mas
Posted in Books, Statistics with tags Christmas, George Casella, Monte Carlo, R, simulation on November 1, 2009 by xi'anOur book Introducing Monte Carlo Methods with R with George Casella, published by Springer Verlag, should appear before Christmas, if I am to believe the following email from the production manager:
Dear Dr. Robert,
I would like to inform you that the estimated publication date for this book is Dec 14 (…)
The production delays have thus incredibly shrunk from what they were even five years ago. This may be related to the new print-on-demand practice that avoids publishing a whole printing at once... So now you know what you should order for X’mas!
A Vanilla Rao-Blackwellisation (comments)
Posted in Statistics with tags birth-and-death process, importance sampling, jump process, MCMC, Monte Carlo, simulation on August 26, 2009 by xi'anOne of the authors of “On convergence of importance sampling and other properly weighted samples to the target distribution” by S. Malefaki and G. Iliopoulos, sent me their paper (now published in JSPI, 2008, pp. 1210-1225) to point out the connection with our Vanilla Rao-Blackwellisation paper. There is indeed a link in that those authors also exploit the sequence of accepted values in an MCMC sequence to build up geometric weights based on the distribution of those accepted rv’s. The paper also relates more strongly to the series of papers published by Jun Liu and coauthors in JASA in the early 2000’s about random importance weights, and even more to the birth-and-death jump processes introduced by Brian Ripley in his 1987 simulation book, and studied in Geyer and Møller (1994), Grenander and Miller (1994) and Phillips and Smith (1996) that led to the birth-and-death MCMC approach of Mathew Stephens in his thesis and 2000 Annals paper. As later analysed in our 2003 Series B paper, this jump process approach is theoretically valid but may lead to difficulties in the implementation stage. The first one is that each proposed value is accepted, albeit briefly and thus that, with proposals that have a null recurrent or a transient behaviour, it may take “forever” to go to infinity and back. The second one is that the perspective offered by this representation—which in the case of the standard Metropolis algorithm does not involve any modification—gives a vision of Metropolis algorithms as a rough version of an importance sampling algorithm. While this somehow is also the case for our Vanilla paper, the whole point of using a Metropolis or a Gibbs algorithm is exactly to avoid picking an importance sampling distribution in complex settings because they are almost necessarily inefficient and instead exploit some features of the target to build the proposals. (This is obviously a matter of perspective on the presentation of the analysis in the above paper, nothing’s being wrong with its mathematics.)
MaxEnt2009
Posted in Statistics with tags MaxEnt2009, maximum entropy, Monte Carlo, nested sampling, simulation on July 6, 2009 by xi'anThe revision of my talk for the MaxEnt2009 conference is now available on slideshare as
The major difference with the talks I gave in Montréal, Edinburgh, Warwick or Rimini is the additional experiment we ran with Darren Wraith on the banana targets already used in the “PMC for cosmologist” paper. As for the mixture benchmark used in the paper with Nicolas Chopin, we found that implementing nested sampling by the book, ie based on the lighthouse code provided in the original papers, led to the right value on average but with a lot more variability than for importance sampling solutions (with a comparable number of iterations).
When running this morning on the campus (i.e. wading through water particles without a snorkel), I thought—helped by comments from Olivier Cappé—that the best explanation for nested sampling is one of importance sampling, the weight of points being the prior mass of the current upper likelihood region. Obviously, this is not the whole story since those constrained priors have a smaller support than the posterior, but this may help in a better evaluation of nested sampling.
