As mentioned earlier, this was my very first MCqMC conference and I really enjoyed it, even though (or because) there were many topics that did not fall within my areas of interest. (By comparison, WSC is a serie of conferences too remote from those areas for my taste, as I realised in Berlin where we hardly attended any talk and hardly anyone attended my session!) Here I appreciated the exposure to different mathematical visions on Monte Carlo, without being swamped by applications as at WSC… Obviously, our own Bayesian computational community was much less represented than at, say, MCMSki! Nonetheless, I learned a lot during this conference for instance from Peter Glynn‘s fantastic talk, and I came back home with new problems and useful references [as well as a two-hour delay in the train ride from Brussels]. I also obviously enjoyed the college-town atmosphere of Leuven, the many historical landmarks and the easily-found running routes out of the town. I am thus quite eager to attend the next MCqMC 2016 meeting (in Stanford, an added bonus!) and even vaguely toying with the idea of organising MCqMC 2018 in Monaco (depending on the return for ISBA 2016 and ISBA 2018). In any case, thanks to the scientific committee for the invitation to give a plenary lecture in Leuven and to the local committee for a perfect organisation of the meeting.
Archive for the Statistics Category
“At equilibrium, we thus should not expect gains of several orders of magnitude.”
As was signaled to me several times during the MCqMC conference in Leuven, Rémi Bardenet, Arnaud Doucet and Chris Holmes (all from Oxford) just wrote a short paper for the proceedings of ICML on a way to speed up Metropolis-Hastings by reducing the number of terms one computes in the likelihood ratio involved in the acceptance probability, i.e.
The observations appearing in this likelihood ratio are a random subsample from the original sample. Even though this leads to an unbiased estimator of the true log-likelihood sum, this approach is not justified on a pseudo-marginal basis à la Andrieu-Roberts (2009). (Writing this in the train back to Paris, I am not convinced this approach is in fact applicable to this proposal as the likelihood itself is not estimated in an unbiased manner…)
In the paper, the quality of the approximation is evaluated by Hoeffding’s like inequalities, which serves as the basis for a stopping rule on the number of terms eventually evaluated in the random subsample. In fine, the method uses a sequential procedure to determine if enough terms are used to take the decision and the probability to take the same decision as with the whole sample is bounded from below. The sequential nature of the algorithm requires to either recompute the vector of likelihood terms for the previous value of the parameter or to store all of them for deriving the partial ratios. While the authors adress the issue of self-evaluating whether or not this complication is worth the effort, I wonder (from my train seat) why they focus so much on recovering the same decision as with the complete likelihood ratio and the same uniform. It would suffice to get the same distribution for the decision (an alternative that is easier to propose than to create of course). I also (idly) wonder if a Gibbs version would be manageable, i.e. by changing only some terms in the likelihood ratio at each iteration, in which case the method could be exact… (I found the above quote quite relevant as, in an alternative technique we are constructing with Marco Banterle, the speedup is particularly visible in the warmup stage.) Hence another direction in this recent flow of papers attempting to speed up MCMC methods against the incoming tsunami of “Big Data” problems.
As the Le Monde mathematical puzzle of this week was a geometric one (the quadrangle ABCD is divided into two parts with the same area, &tc…) , with no clear R resolution, I chose to bypass it. In this April 3 issue, several items of interest: first, a report by Etienne Ghys on Yakov Sinaï’s Abel Prize for his work “between determinism and randomness”, centred on ergodic theory for dynamic systems, which sounded like the ultimate paradox the first time I heard my former colleague Denis Bosq give a talk about it in Paris 6. Then a frightening fact: the summer conditions have been so unusually harsh in Antarctica (or at least near the Dumont d’Urville French austral station) that none of the 15,000 Adélie penguin couples studied there managed to keep their chick alive. This was due to an ice shelf that did not melt at all over the summer, forcing the penguins to walk an extra 40k to reach the sea… Another entry on the legal obligation for all French universities to offer a second chance exam, no matter how students are evaluated in the first round. (Too bad, I always find writing a second round exam a nuisance.)
I hesitated in changing the above title for “MCqMSmaug” as the plenary talk I attended this morning was given by Wenzel Jakob, who uses Markov chain Monte Carlo methods in image rendering and light simulation. The talk was low-tech’, with plenty of pictures and animations (incl. excerpts from recent blockbusters!), but it stressed how much proper rending relies on powerful MCMC techniques. One point particularly attracted my attention, namely the notion of manifold exploration as it seemed related to my zero measure recent post. (A related video is available on Jakob’s webpage.) You may then wonder where the connection with Smaug could be found: Wenzel Jakob is listed in the credits of both Hobbit movies for his contributions to the visual effects! (Hey, MCMC made Smaug [visual effects the way they are], a cool argument for selling your next MCMC course! I will for sure include a picture of Smaug in my next R class presentation…) The next sessions of the morning opposed Sobol’s memorial to more technical light rendering and I chose Sobol, esp. because I had missed Art Owen’s tutorial on Sunday, as he gave a short presentation on using Sobol’s criteria to identify variables contributing the most to the variability or extreme values of a function, an extreme value kind of ANOVA, most interesting if far from my simulation area… The afternoon sessions saw MCMC talks by Luke Bornn and Scott Schmidler, both having connection with the Wang-Landau algorithm. Actually, Scott’s talk was the one generating the most animated discussion among all those I attended in MCqMC! (To the point of the chairman getting rather rudely making faces…)
As the second day at MCqMC 2014, was mostly on multi-level Monte Carlo and quasi-Monte Carlo methods, I did not attend many talks but had a long run in the countryside (even saw a pheasant and a heron), worked at “home” on pressing recruiting evaluations and had a long working session with Pierre Jacob. Plus an evening out sampling (just) a few Belgian beers in the shade of the city hall…
Today was more in my ballpark as there were MCMC talks the whole day! The plenary talk was not about MCMC as Erich Novak presented a survey on the many available results bounding the complexity of approximating an integral based on a fixed number of evaluations of the integrand, some involving the dimension (and its curse), some not, some as fast as √n and some not as fast, all this depending on the regularity and the size of the classes of integrands considered. In some cases, the solution was importance sampling, in other cases, quasi-Monte Carlo, and yet other cases were still unsolved. Then Yves Atchadé gave a new perspective on computing the asymptotic variance in the central limit theorem on Markov chains when truncating the autocovariance, Matti Vihola talked about theoretical orderings of Markov chains that transmuted into the very practical consequence that using more simulations in a pseudo-marginal likelihood approximation improved acceptance rate and asymptotic variances (and this applies to aBC-MCMC as well), Radu Craiu proposed a novel processing of adaptive MCMC by treating various approximations to the true target as food for a multiple-try Metropolis algorithm, and Luca Martino had a go at resuscitating the ARMS algorithm of Gilks and Wild (used for a while in BUGS), although the talk did not dissipate all of my misgivings about the multidimensional version! I had more difficulties following the “Warwick session” which was made of four talks by current or former students from Warwick, although I appreciated the complexity of the results in infinite dimensional settings and novel approximations to diffusion based Metropolis algorithms. No further session this afternoon as the “social” activity was to visit the nearby Stella Artois brewery! This activity made us very social, for certain, even though there was hardly a soul around in this massively automated factory. (Maybe an ‘Og post to come one of those days…)