A new position for the of Professor Of Statistics and Data Science / Director of the [newly created] Warwick Data Science Institute has been posted. To quote from the job description, “the position arises from the Department of Statistics’ commitment, in collaboration with the Warwick Mathematics Institute and the Department of Computer Science, to a coherent methodological approach to the fundamentals of Data Science and the challenges of complex data sets (for example big data).” The interview date is November 27, 2014. All details available here.
Archive for University of Warwick
Yet another workshop around! Still at Warwick, organised by Simon Barthelmé, Nicolas Chopin and Adam Johansen on the theme of statistical aspects of neuroscience. Being nearby I attended a few lectures today but most talks are more topical than my current interest in the matter, plus workshop fatigue starts to appear!, and hence I will keep a low attendance for the rest of the week to take advantage of my visit here to make some progress in my research and in the preparation of the teaching semester. (Maybe paradoxically I attended a non-neuroscience talk by listening to Richard Wilkinson’s coverage of ABC methods, with an interesting stress on meta-models and the link with computer experiments. Given that we are currently re-revising our paper with Matt Moore and Kerrie Mengersen (and now Chris Drovandi), I find interesting to see a sort of convergence in our community towards a re-re-interpretation of ABC as producing an approximation of the distribution of the summary statistic itself, rather than of the original data, using auxiliary or indirect or pseudo-models like Gaussian processes. (Making the link with Mark Girolami’s talk this morning.)
Great poster session yesterday night and at lunch today. Saw an ABC poster (by Dennis Prangle, following our random forest paper) and several MCMC posters (by Marco Banterle, who actually won one of the speed-meeting mini-project awards!, Michael Betancourt, Anne-Marie Lyne, Murray Pollock), and then a rather different poster on Mondrian forests, that generalise random forests to sequential data (by Balaji Lakshminarayanan). The talks all had interesting aspects or glimpses about big data and some of the unnecessary hype about it (them?!), along with exposing the nefarious views of Amazon to become the Earth only seller!, but I particularly enjoyed the astronomy afternoon and even more particularly Steve Roberts sweep through astronomy machine-learning. Steve characterised variational Bayes as picking your choice of sufficient statistics, which made me wonder why there were no stronger connections between variational Bayes and ABC. He also quoted the book The Fourth Paradigm: Data-Intensive Scientific Discovery by Tony Hey as putting forward interesting notions. (A book review for the next vacations?!) And also mentioned zooniverse, a citizens science website I was not aware of. With a Bayesian analysis of the learning curve of those annotating citizens (in the case of supernovae classification). Big deal, indeed!!!
I had quite a special day today as I travelled through Birmingham, made a twenty minutes stop in Coventry to drop my bag in my office, went down to London to collect a most kindly loaned city-bike and took the train back to Coventry with the said bike… On my way from Bristol to Warwick, I decided to spend the night in downtown Birmingham as it was both easier and cheaper than to find accommodation on Warwick campus. However, while the studio I rented was well-designed and brand-new, my next door neighbours were not so well-designed in that I could hear them and the TV through the wall, despite top-quality ear-plugs! After a request of mine, they took the TV off but kept to the same decibel level for their uninteresting exchanges. In the morning I tried to go running in the centre of Birmingham but, as I could not find the canals, I quickly got bored and gave up. As Mark had proposed to lend me a city bike for my commuting in [and not to] Warwick, I then decided to take the opportunity of a free Sunday to travel down to London to pick the bike, change the pedals in a nearby shop, add an anti-theft device, and head back to Coventry. Which gave me the opportunity to bike in London by Abbey Road, Regent Park, and Hampstead, before [easily] boarding a fast train back to Coventry and biking up to the University of Warwick campus. (Sadly to discover that all convenience stores had closed by then… )
…already Thursday, our [early] departure day!, with an nth (!) non-parametric session that saw [the newly elected ISBA Fellow!] Judith Rousseau present an ongoing work with Chris Holmes on the convergence or non-convergence conditions for a Bayes factor of a non-parametric hypothesis against another non-parametric. I wondered at the applicability of this test as the selection criterion in ABC settings, even though having an iid sample to start with is a rather strong requirement.
Switching between a scalable computation session with Alex Beskos, who talked about adaptive Langevin algorithms for differential equations, and a non-local prior session, with David Rossell presenting a smoother way to handle point masses in order to accommodate frequentist coverage. Something we definitely need to discuss the next time I am in Warwick! Although this made me alas miss both the first talk of the non-local session by Shane Jensen the final talk of the scalable session by Doug Vandewrken where I happened to be quoted (!) for my warning about discretising Markov chains into non-Markov processes. In the 1998 JASA paper with Chantal Guihenneuc.
After a farewell meal of ceviche with friends in the sweltering humidity of a local restaurant, I attended [the newly elected ISBA Fellow!] Maria Vanucci’s talk on her deeply involved modelling of fMRI. The last talk before the airport shuttle was François Caron’s description of a joint work with Emily Fox on a sparser modelling of networks, along with an auxiliary variable approach that allowed for parallelisation of a Gibbs sampler. François mentioned an earlier alternative found in machine learning where all components of a vector are updated simultaneously conditional on the previous avatar of the other components, e.g. simulating (x’,y’) from π(x’|y) π(y’|x) which does not produce a convergent Markov chain. At least not convergent to the right stationary. However, running a quick [in-flight] check on a 2-d normal target did not show any divergent feature, when compared with the regular Gibbs sampler. I thus wonder at what can be said about the resulting target or which conditions are need for divergence. A few scribbles later, I realised that the 2-d case was the exception, namely that the stationary distribution of the chain is the product of the marginal. However, running a 3-d example with an auto-exponential distribution in the taxi back home, I still could not spot a difference in the outcome.