barbed WIREs

Maybe childishly, I am fairly unhappy with the way the submission of our Accelerating MCMC review was handled by WIREs Computational Statistics, i.e., Wiley, at the production stage. For some reason, or another, I sent the wrong bibTeX file with my LaTeX document [created using the style file imposed by WIREs]. Rather than pointing out the numerous missing entries, the production staff started working on the paper and sent us a proof with an endless list of queries related to these missing references. When I sent back the corrected LaTeX and bibTeX files, it answered back that it was too late to modify the files as it would “require re-work of [the] already processed paper which is also not a standard process for the journal”. Meaning in clearer terms that Wiley does not want to pay any additional time spent on this paper and that I have to provide from my own “free” time to make up for this mess…

SDSS with friends

When browsing over lunch the April issue of Amstat News, I came upon this page advertising rather loudly the SDSS symposium of next month. And realised that not only it features “perhaps the most prominent statistician to have repeatedly published material written by others without attribution” (a quote from Gelman and Basbøll, 2013, in American Scientist), namely  Ed Wegman, as the guest of honor,  but also one co-author of a retracted Computational Statistics paper [still included in Wegman’s list of publications] as program chair and another co-author from the “Hockey Stick” plagiarised report as plenary speaker. A fairly friendly reunion, then, if “networking” is to be understood this way, except that this is a major conference, supported by ASA and other organisations. Rather shocking, isn’t it?! (The entry also made me realise that the three co-authors were the original editors of WIREs, before Wegman and Said withdrew in 2012.)

accelerating MCMC

As forecasted a rather long while ago (!), I wrote a short and incomplete survey on some approaches to accelerating MCMC. With the massive help of Victor Elvira (Lille), Nick Tawn (Warwick) and Changye Wu (Dauphine). Survey which current version just got arXived and which has now been accepted by WIREs Computational Statistics. The typology (and even the range of methods) adopted here is certainly mostly arbitrary, with suggestions for different divisions made by a very involved and helpful reviewer. While we achieved a quick conclusion to the review process, suggestions and comments are most welcome! Even if we cannot include every possible suggestion, just like those already made on X validated. (WIREs stands for Wiley Interdisciplinary Reviews and its dozen topics cover several fields, from computational stats to biology, to medicine, to engineering.)

ackward citation style

When submitting a paper to WIREs, I was asked to use the APA style for citations. This is rather unpleasant as it requires all kinds of fixes and even then returns an unseemly outcome, quoting sometimes authors with their first name and at a point ignoring the parentheses for \citep citations… Maybe all those annoying bugs are on purpose, as APA stands for the American Psychological Association, presumably eager to experiment on new subjects!

accelerating MCMC

I have recently [well, not so recently!] been asked to write a review paper on ways of accelerating MCMC algorithms for the [review] journal WIREs Computational Statistics and would welcome all suggestions towards the goal of accelerating MCMC algorithms. Besides [and including more on]

• coupling strategies using different kernels and switching between them;
• tempering strategies using flatter or lower dimensional targets as intermediary steps, e.g., à la Neal;
• sequential Monte Carlo with particle systems targeting again flatter or lower dimensional targets and adapting proposals to this effect;
• Hamiltonian MCMC, again with connections to Radford (and more generally ways of avoiding rejections);
• adaptive MCMC, obviously;
• Rao-Blackwellisation, just as obviously (in the sense that increasing the precision in the resulting estimates means less simulations).