oxwasp@amazon.de

The reason for my short visit to Berlin last week was an OxWaSP (Oxford and Warwick Statistics Program) workshop hosted by Amazon Berlin with talks between statistics and machine learning, plus posters from our second year students. While the workshop was quite intense, I enjoyed very much the atmosphere and the variety of talks there. (Just sorry that I left too early to enjoy the social programme at a local brewery, Brauhaus Lemke, and the natural history museum. But still managed nice runs east and west!) One thing I found most interesting (if obvious in retrospect) was the different focus of academic and production talks, where the later do not aim at a full generality or at a guaranteed improvement over the existing, provided the new methodology provides a gain in efficiency over the existing.

This connected nicely with me reading several Nature articles on quantum computing during that trip,  where researchers from Google predict commercial products appearing in the coming five years, even though the technology is far from perfect and the outcome qubit error prone. Among the examples they provided, quantum simulation (not meaning what I consider to be simulation!), quantum optimisation (as a way to overcome multimodality), and quantum sampling (targeting given probability distributions). I find the inclusion of the latest puzzling in that simulation (in that sense) shows very little tolerance for errors, especially systematic bias. It may be that specific quantum architectures can be designed for specific probability distributions, just like some are already conceived for optimisation. (It may even be the case that quantum solutions are (just next to) available for intractable constants as in Ising or Potts models!)

India snapshop [jatp]

ABC in Stockholm [on-board again]

After a smooth cruise from Helsinki to Stockholm, a glorious sunrise over the Ålend Islands, and a morning break for getting an hasty view of the city, ABC in Helsinki (a.k.a. ABCruise) resumed while still in Stockholm. The first talk was by Laurent Calvet about dynamic (state-space) models, when the likelihood is not available and replaced with a proximity between the observed and the simulated observables, at each discrete time in the series. The authors are using a proxy predictive for the incoming observable and derive an optimal—in a non-parametric sense—bandwidth based on this proxy. Michael Gutmann then gave a presentation that somewhat connected with his talk at ABC in Roma, and poster at NIPS 2014, about using Bayesian optimisation to reduce the rejections in ABC algorithms. Which means building a model of a discrepancy or distance by Bayesian optimisation. I definitely like this perspective as it reduces the simulation to one of a discrepancy (after a learning step). And does not require a threshold. Aki Vehtari expanded on this idea with a series of illustrations. A difficulty I have with the approach is the construction of the acquisition function… The last session while pretty late was definitely exciting with talks by Richard Wilkinson on surrogate or emulator models, which goes very much in a direction I support, namely that approximate models should be accepted on their own, by Julien Stoehr with clustering and machine learning tools to incorporate more summary statistics, and Tim Meeds who concluded with two (small) talks!, centred on the notion of deterministic algorithms that explicitly incorporate the random generators within the comparison, resulting in post-simulation recentering à la Beaumont et al. (2003), plus new advances with further incorporations of those random generators turned deterministic functions within variational Bayes inference…

On Wednesday morning, we will land back in Helsinki and head back to our respective homes, after another exciting ABC in… workshop. I am terribly impressed by the way this workshop at sea operated, providing perfect opportunities for informal interactions and collaborations, without ever getting claustrophobic or dense. Enjoying very long days also helped. While it seems unlikely we can repeat this successful implementation, I hope we can aim at similar formats in the coming occurrences. Kitos paljon to our Finnish hosts!

ABC in Helsinki [on-board]

ABC in Helsinki (a.k.a. ABCruise) has started! With a terrific weather most adequate for a cruise on the Baltic. The ship on which the workshop takes place is certainly larger than any I have been on, including the Channel ferries, and the inside alley looks rather like a shopping centre! However, the setting is exceptional, with comfy sea-facing cabins and pleasant breaks (including fancy tea!) Plus,  we have a quiet and cosy conference room that makes one forgets one is on a boat. Until it starts rocking. Or listing! The cruise boat is definitely large enough to be fairly stable. A unique experience we could consider for future (AB-see) workshops (with the caveat that we benefited from exceptional circumstances that brought the costs down to ridiculous amounts).

Richard Everitt talked about the synthetic likelihood approach and its connection with ABC. Making clear for me a point I had somewhat forgotten, namely that the approximative likelihood is a Gaussian at the observed summary statistics, but one centred at empirical moments derived from the simulation of pseudo summaries based on a given value of the parameter θ. So it is not an exact approach in that it does not converge to the true likelihood as the number of simulation grows to infinity. (While a kernel would converge.) That means it may (will) misrepresent the tails unless the distribution of the summary statistic is close to Normal. Richard also introduced bootstrap or bags of little bootstraps in order to speed up the generation of the pseudo-data, which makes sense albeit it moves the sampling away from the true model since it is conditional on  a single simulation.

Jean-Michel Marin introduced the ABC inference algorithm we are currently working on, using regression random forests that differ from the classification forests we used for model selection. (The paper is close to completion so I hope to be able to tell more in a near future!) Clara Grazian presented her semi-parametric work using ABC with Brunero Liseo. That was part of her thesis. Thomas Schön presented an extension of his particle Gibbs with adaptive sampling to the case of degenerate transitions, using an ABC approximation to get around this central problem. A very interesting entry that I need to study deeper. And Caroline Colijn talked about ABC for trees, mostly about the selection of summary statistics towards comparing tree topologies, with  a specific distance between trees that caters to the topology and only the topology.

CRiSM workshop on estimating constants [slides]

A short announcement that the slides of almost all talks at the CRiSM workshop on estimating constants last April 20-22 are now available. Enjoy (and dicuss)!

contemporary issues in hypothesis testing

Next Fall, on 15-16 September, I will take part in a CRiSM workshop on hypothesis testing. In our department in Warwick. The registration is now open [until Sept 2] with a moderate registration free of £40 and a call for posters. Jim Berger and Joris Mulder will both deliver a plenary talk there, while Andrew Gelman will alas give a remote talk from New York. (A terrific poster by the way!)

ABC in Helsinki & Stockholm [deadline looming]

In case you have not yet registered for ABC in Helsinki (a.k.a. ABCruise), registration is open for just another week, with the all-inclusive fees of 200 euros for trip, cabin, talks, and meals! When registering you need to buy first a ticket on the Aalto University web shop: at some point, distinguishing between “Maksa” which means pay, and “Peruuta” which means cancel, may help! The submission of ABC posters is also encouraged till May 1, with emails to be sent to abcinhelsinki on gmail.