Special Issue of ACM TOMACS on Monte Carlo Methods in Statistics

As posted here a long, long while ago, following a suggestion from the editor (and North America Cycling Champion!) Pierre Lécuyer (Université de Montréal), Arnaud Doucet (University of Oxford) and myself acted as guest editors for a special issue of ACM TOMACS on Monte Carlo Methods in Statistics. (Coincidentally, I am attending a board meeting for TOMACS tonight in Berlin!) The issue is now ready for publication (next February unless I am confused!) and made of the following papers:

* Massive parallelization of serial inference algorithms for a complex generalized linear model MARC A. SUCHARD, IVAN ZORYCH, PATRICK RYAN, DAVID MADIGAN	Abstract
*Convergence of a Particle-based Approximation of the Block Online Expectation Maximization Algorithm SYLVAIN LE CORFF and GERSENDE FORT	Abstract
* Efficient MCMC for Binomial Logit Models AGNES FUSSL, SYLVIA FRÜHWIRTH-SCHNATTER, RUDOLF FRÜHWIRTH	Abstract
* Adaptive Equi-Energy Sampler: Convergence and Illustration AMANDINE SCHRECK and GERSENDE FORT and ERIC MOULINES	Abstract
* Particle algorithms for optimization on binary spaces CHRISTIAN SCHÄFER	Abstract
* Posterior expectation of regularly paved random histograms RAAZESH SAINUDIIN, GLORIA TENG, JENNIFER HARLOW, and DOMINIC LEE	Abstract
* Small variance estimators for rare event probabilities MICHEL BRONIATOWSKI and VIRGILE CARON	Abstract
* Self-Avoiding Random Dynamics on Integer Complex Systems FIRAS HAMZE, ZIYU WANG, and NANDO DE FREITAS	Abstract
* Bayesian learning of noisy Markov decision processes SUMEETPAL S. SINGH, NICOLAS CHOPIN, and NICK WHITELEY	Abstract

Here is the draft of the editorial that will appear at the beginning of this special issue. (All faults are mine, of course!)

multiple try/point Metropolis algorithm

Among the arXiv documents I printed at the turn of the year in order to get a better look at them (in the métro if nowhere else!), there were two papers by Luca Martino and co-authors from Universidad Carlos III, Madrid, A multi-point Metropolis scheme with generic weight functions and Different acceptance functions for multiple try Metropolis schemes. The multiple-try algorithm sounds like another version of the delayed sampling algorithm of Tierney and Mira (1999) and Green and Mira (2001). I somehow missed it, even though it was introduced in Liu et al. (2000) and Quin and Liu (2001). Multiple-try Metropolis builds upon the idea that, instead of making one proposal at a time, it is feasible to build a sequence of proposals and to pick one among those, presumably rather likely and hence more open to being accepted. The sequence of proposals may depend upon the past propositions as well as on the current value, lending some degree of adaptability to the scheme. In the current implementation, the algorithm remains rather clumsy [in my opinion] in that (a) a fixed horizon N need be fixed and (b) an additional series of backward simulations need be produced simply to keep the balance equation happy… Hence a total number of simulations O(N) for one possible acceptance. The first note slightly extends Quin and Liu (2001) by using a fairly general weighting scheme. The second paper studies some particular choices for the weights in a much less adaptive scheme (where parallelisation would be an appropriate alternative, since each proposal in the multiple try only depends on the current value of the chain). But it does not demonstrate a more efficient behaviour than when using a cycle or a mixture of Metropolis-Hastings algorithms. The method seems to regain popularity, though, as Roberto Casarin, Radu Craiu and Fabrizio Leisen (also from Carlos III)  arXived a paper on a multiple-try algorithm, connected with population Monte Carlo, and more recently published it in Statistics and Computing.

2012, Turing year

Buying the special issue of La Recherche on “La révolution des mathématiques”, I discovered that this is the Alan Turing Year in celebration of the 100th anniversary of Turing‘s birth. The math department at the University of Leeds has a webpage on all the events connected with this celebration. From all over the World. (There is even a Turing relay in Cambridge, unfortunately it is not open to the general public… Unless you are attending the Isaac Newton Institute at the time.) Quite fitting a tribute. (Given Turing’s contributions to Bayesian analysis, as depicted e.g. in the theory that would not die, ISBA could have included a special session in the ISBA 2012 meeting in Kyoto. I will certainly dedicate the session I co-organise there on parallel computing to his memory.)

JCGS 20th anniversary

For its 20th anniversary, JCGS offers free access to papers, including Andrew’s discussion paper Why tables are really much better than graphs. (Another serious ending for an April fool joke!) Incidentally (or rather coincidentally), I received today the great news that our Using parallel computation to improve Independent Metropolis-Hastings based estimation paper is accepted by JCGS. (First accepted paper ever for my PhD student Pierre!) Maybe making it into one of the 20th anniversary issues!

Vanilla on-line

The Vanilla Rao–Blackwellization of Metropolis–Hastings algorithms paper with Randal Douc is now published in Annals of Statistics (Volume 39, Number 1 (2011), pages 261-277) and available on-line via the project Euclid. We are currently working with Pierre Jacob on an extension of this idea towards parallelisation…