The nice logo of MCqMC 2016 was a collection of eight series of QMC dots on the unit (?) cube. The organisers set a competition to identify the principles behind those quasi-random sets and as I had no idea for most of them I entered very random sets unconnected with algorithmia, for which I got an honourable mention and a CD prize (if not the conference staff tee-shirt I was coveting!) Art Owen sent me back my entry, posted below and hopefully (or not!) readable.
Archive for Stanford University
In his plenary talk this morning, Arnaud Doucet discussed the application of pseudo-marginal techniques to the latent variable models he has been investigating for many years. And its limiting behaviour towards efficiency, with the idea of introducing correlation in the estimation of the likelihood ratio. Reducing complexity from O(T²) to O(T√T). With the very surprising conclusion that the correlation must go to 1 at a precise rate to get this reduction, since perfect correlation would induce a bias. A massive piece of work, indeed!
The next session of the morning was another instance of conflicting talks and I hoped from one room to the next to listen to Hani Doss’s empirical Bayes estimation with intractable constants (where maybe SAME could be of interest), Youssef Marzouk’s transport maps for MCMC, which sounds like an attractive idea provided the construction of the map remains manageable, and Paul Russel’s adaptive importance sampling that somehow sounded connected with our population Monte Carlo approach. (With the additional step of considering transform maps.)
An interesting item of information I got from the final announcements at MCqMC 2016 just before heading to Monash, Melbourne, is that MCqMC 2018 will take place in the city of Rennes, Brittany, on July 2-6. Not only it is a nice location on its own, but it is most conveniently located in space and time to attend ISBA 2018 in Edinburgh the week after! Just moving from one Celtic city to another Celtic city. Along with other planned satellite workshops, this occurrence should make ISBA 2018 more attractive [if need be!] for participants from oversea.
On Thursday, Christoph Aistleiter [from TU Gräz] gave a plenary talk at MCqMC 2016 around Hermann Weyl’s 1916 paper, Über die Gleichverteilung von Zahlen mod. Eins, which demonstrates that the sequence a, 22a, 32a, … mod 1 is uniformly distributed on the unit interval when a is irrational. Obviously, the notion was not introduced for simulation purposes, but the construction applies in this setting! At least in a theoretical sense. Since for instance the result that the sequence (a,a²,a³,…) mod 1 being uniformly distributed for almost all a’s has not yet found one realisation a. But a nice hour of history of mathematics and number theory: it is not that common we hear the Riemann zeta function mentioned in a simulation conference!
The following session was a nightmare in that I wanted to attend all four at once! I eventually chose the transport session, in particular because Xiao-Li advertised it at the end of my talk. The connection is that his warp bridge sampling technique provides a folding map between modes of a target. Using a mixture representation of the target and folding all components to a single distribution. Interestingly, this transformation does not require a partition and preserves the normalising constants [which has a side appeal for bridge sampling of course]. In a problem with an unknown number of modes, the technique could be completed by [our] folding in order to bring the unobserved modes into the support of the folded target. Looking forward the incoming paper! The last talk of this session was by Matti Vihola, connecting multi-level Monte Carlo and unbiased estimation à la Rhee and Glynn, paper that I missed when it got arXived last December.
The last session of the day was about probabilistic numerics. I have already discussed extensively about this approach to numerical integration, to the point of being invited to the NIPS workshop as a skeptic! But this was an interesting session, both with introductory aspects and with new ones from my viewpoint, especially Chris Oates’ description of a PN method for handling both integrand and integrating measure as being uncertain. Another arXival that went under my decidedly deficient radar.
In her plenary talk this morning, Christine Lemieux discussed connections between quasi-Monte Carlo and copulas, covering a question I have been considering for a while. Namely, when provided with a (multivariate) joint cdf F, is there a generic way to invert a vector of uniforms [or quasi-uniforms] into a simulation from F? For Archimedian copulas (as we always can get back to copulas), there is a resolution by the Marshall-Olkin representation, but this puts a restriction on the distributions F that can be considered. The session on synthetic likelihoods [as introduced by Simon Wood in 2010] put together by Scott Sisson was completely focussed on using normal approximations for the distribution of the vector of summary statistics, rather than the standard ABC non-parametric approximation. While there is a clear (?) advantage in using a normal pseudo-likelihood, since it stabilises with much less simulations than a non-parametric version, I find it difficult to compare both approaches, as they lead to different posterior distributions. In particular, I wonder at the impact of the dimension of the summary statistics on the approximation, in the sense that it is less and less likely that the joint is normal as this dimension increases. Whether this is damaging for the resulting inference is another issue, possibly handled by a supplementary ABC step that would take the first-step estimate as summary statistic. (As a side remark, I am intrigued at everyone being so concerned with unbiasedness of methods that are approximations with no assessment of the amount of approximation!) The last session of the day was about multimodality and MCMC solutions, with talks by Hyungsuk Tak, Pierre Jacob and Babak Shababa, plus mine. Hunsuk presented the RAM algorithm I discussed earlier under the title of “love-hate” algorithm, which was a kind reference to my post! (I remain puzzled by the ability of the algorithm to jump to another mode, given that the intermediary step aims at a low or even zero probability region with an infinite mass target.) And Pierre talked about using SMC for Wang-Landau algorithms, with a twist to the classical stochastic optimisation schedule that preserves convergence. And a terrific illustration on a distribution inspired from the Golden Gate Bridge that reminded me of my recent crossing! The discussion around my folded Markov chain talk focussed on the extension of the partition to more than two sets, the difficulty being in generating automated projections, with comments about connections with computer graphic tools. (Too bad that the parallel session saw talks by Mark Huber and Rémi Bardenet that I missed! Enjoying a terrific Burmese dinner with Rémi, Pierre and other friends also meant I could not post this entry on time for the customary 00:16. Not that it matters in the least…)
Here is a very nice inclusion about the ‘Og by the organisers of MCqMC 2016.
Before attending MCqMC in Stanford in two weeks, I will take some vacations in Northern California [really North!] with my family. Starting with the San Francisco ½ marathon tomorrow. So expect delays [as we already got stuck
six twenty-seven thirty hours in De Gaulle airport thanks to a strike!] and mostly non-statistical entries on the ‘Og. And pictures.