## Statistics and Computing special MCMSk’issue [call for papers]

Posted in Books, Mountains, R, Statistics, University life with tags , , , , , , , , , , , on February 7, 2014 by xi'an

Following the exciting and innovative talks, posters and discussions at MCMski IV, the editor of Statistics and Computing, Mark Girolami (who also happens to be the new president-elect of the BayesComp section of ISBA, which is taking over the management of future MCMski meetings), kindly proposed to publish a special issue of the journal open to all participants to the meeting. Not only to speakers, mind, but to all participants.

So if you are interested in submitting a paper to this special issue of a computational statistics journal that is very close to our MCMski themes, I encourage you to do so. (Especially if you missed the COLT 2014 deadline!) The deadline for submissions is set on March 15 (a wee bit tight but we would dearly like to publish the issue in 2014, namely the same year as the meeting.) Submissions are to be made through the Statistics and Computing portal, with a mention that they are intended for the special issue.

An editorial committee chaired by Antonietta Mira and composed of Christophe Andrieu, Brad Carlin, Nicolas Chopin, Jukka Corander, Colin Fox, Nial Friel, Chris Holmes, Gareth Jones, Peter Müller, Antonietta Mira, Geoff Nicholls, Gareth Roberts, Håvård Rue, Robin Ryder, and myself, will examine the submissions and get back within a few weeks to the authors. In a spirit similar to the JRSS Read Paper procedure, submissions will first be examined collectively, before being sent to referees. We plan to publish the reviews as well, in order to include a global set of comments on the accepted papers. We intend to do it in The Economist style, i.e. as a set of edited anonymous comments. Usual instructions for Statistics and Computing apply, with the additional requirements that the paper should be around 10 pages and include at least one author who took part in MCMski IV.

## cut, baby, cut!

Posted in Books, Kids, Mountains, R, Statistics, University life with tags , , , , , , , , , , , , , on January 29, 2014 by xi'an

At MCMSki IV, I attended (and chaired) a session where Martyn Plummer presented some developments on cut models. As I was not sure I had gotten the idea [although this happened to be one of those few sessions where the flu had not yet completely taken over!] and as I wanted to check about a potential explanation for the lack of convergence discussed by Martyn during his talk, I decided to (re)present the talk at our “MCMSki decompression” seminar at CREST. Martyn sent me his slides and also kindly pointed out to the relevant section of the BUGS book, reproduced above. (Disclaimer: do not get me wrong here, the title is a pun on the infamous “drill, baby, drill!” and not connected in any way to Martyn’s talk or work!)

I cannot say I get the idea any clearer from this short explanation in the BUGS book, although it gives a literal meaning to the word “cut”. From this description I only understand that a cut is the removal of an edge in a probabilistic graph, however there must/may be some arbitrariness in building the wrong conditional distribution. In the Poisson-binomial case treated in Martyn’s case, I interpret the cut as simulating from

$\pi(\phi|z)\pi(\theta|\phi,y)=\dfrac{\pi(\phi)f(z|\phi)}{m(z)}\dfrac{\pi(\theta|\phi)f(y|\theta,\phi)}{m(y|\phi)}$

$\pi(\phi|z,\mathbf{y})\pi(\theta|\phi,y)\propto\pi(\phi)f(z|\phi)\pi(\theta|\phi)f(y|\theta,\phi)$

hence loosing some of the information about φ… Now, this cut version is a function of φ and θ that can be fed to a Metropolis-Hastings algorithm. Assuming we can handle the posterior on φ and the conditional on θ given φ. If we build a Gibbs sampler instead, we face a difficulty with the normalising constant m(y|φ). Said Gibbs sampler thus does not work in generating from the “cut” target. Maybe an alternative borrowing from the rather large if disparate missing constant toolbox. (In any case, we do not simulate from the original joint distribution.) The natural solution would then be to make a independent proposal on φ with target the posterior given z and then any scheme that preserves the conditional of θ given φ and y; “any” is rather wistful thinking at this stage since the only practical solution that I see is to run a Metropolis-Hasting sampler long enough to “reach” stationarity… I also remain with a lingering although not life-threatening question of whether or not the BUGS code using cut distributions provide the “right” answer or not. Here are my five slides used during the seminar (with a random walk implementation that did not diverge from the true target…):

## MCMSki 4, 5… [rejuvenating suggestion]

Posted in Kids, Mountains, pictures, Statistics, Travel, University life with tags , , , , , on January 16, 2014 by xi'an

Another thing I should have included in the program. Or in the organising committee: a link with the Young Bayesians (j-ISBA) section… As pointed out to me by Kerrie Mengersen, ISBA meetings are obvious opportunities for young researchers to interact and network, as well as for seeking a job. Thus, there should be time slots dedicated to them in every ISBA sponsored meeting, from a mixer on the very first day to a job market coffee break the next day (and to any other social activity bound to increase the interactivity. Like a ski race.). So I would suggest every ISBA sponsored event (and no only the Bayesian Young Statistician Meetings!) should include a j-ISBA representative in its committee(s) to enforce this policy… (Kerrie also suggested random permutations during the banquet which is a neat idea provided the restaurant structure allows for this. It would have been total chaos in La Calèche last week!)

## accelerated ABC

Posted in Books, Mountains, Statistics with tags , , , , , , on January 14, 2014 by xi'an

Richard Wilkinson arXived a paper on accelerated ABC during MCMSki 4, paper that I almost missed when quickly perusing the daily list. This is another illustration of the “invasion of Gaussian processes” in ABC settings. Maybe under the influence of machine learning.

The paper starts with a link to the synthetic likelihood approximation of Wood (2010, Nature), as in Richard Everitt’s talk last week. Richard (W.) presents the generalised ABC as a kernel-based acceptance probability, using a kernel π(y|x), when y is the observed data and x=x(θ) the simulated one. He proposes a Gaussian process modelling for the log-likelihood (at the observed data y), with a quadratic (in θ) mean and Matérn covariance matrix. Hence the connection with Wood’s synthetic likelihood. Another connection is with Nicolas’ talk on QMC(MC): the θ’s are chosen following a Sobol sequence “in order to minimize the number of design points”. Which requires a reparameterisation to [0,1]p… I find this “uniform” exploration of the whole parameter space delicate to envision in complex parameter spaces and realistic problems, since the likelihood is highly concentrated on a tiny subregion of the original [0,1]p. Not mentioning the issue of the spurious mass on the boundaries of the hypercube possibly induced by the change of variable. The sequential algorithm of Richard also attempts at eliminating implausible zones of the parameter space. i.e. zones where the likelihood is essentially zero. My worries with this interesting notion are that (a) the early Gaussian process approximations may be poor and hence exclude zones they should not; (b) all Gaussian process approximations at all iterations must be saved; (c) the Sobol sequences apply to the whole [0,1]p at each iteration but the non-implausible region shrinks at each iteration, which induces a growing inefficiency in the algorithm. The Sobol sequence should be restricted to the previous non-implausible zone.

Overall, an interesting proposal that would need more prodding to understand whether or not it is robust to poor initialisation and complex structures. And a proposal belonging to the estimated likelihood branch of ABC, which makes use of the final Gaussian process approximation to run an MCM algorithm. Without returning to pseudo-data simulation, replacing it with log-likelihood simulation.

“These algorithms sample space randomly and naively and do not learn from previous simulations”

The above criticism is moderated in a footnote about ABC-SMC using the “current parameter value to determine which move to make next [but] parameters visited in previous iterations are not taken into account”. I still find it excessive in that SMC algorithms and in particular ABC-SMC algorithms are completely free to use the whole past to build the new proposal. This was clearly enunciated in our earlier population Monte Carlo papers. For instance, the complete collection of past particles can be recycled by weights computing thru our AMIS algorithm, as illustrated by Jukka Corander in one genetics application.

## MCMSki IV [prizes]

Posted in Books, Mountains, pictures, Statistics, University life with tags , , , , , on January 10, 2014 by xi'an

Congratulations to the MCMSki IV poster prize winners:

who each received two books from those kindly sent by Academic Press, CRC Press, and Springer-Verlag. (Except one of the above who should contact me for delivering her/him the dedicated books!) And to the honourable mention winners:

And to the members of the jury who worked hard both evening to produce this set of winners… Congrats too to Mina Vekhala from Helsinki who left the banquet with a pair of Blossom skis, thanks to a random draw from U({1,…,188}) of a dinner participant. She was 154th on the list and this number came out first. (Renewed thanks to Blossom skis for their generosity!)