a glacial PhD in Iceland [job announcement]

[Here is a PhD offer at the University of Iceland that may be of interest to some readers or their students. I would have been interested 30 years ago!]

The Department of Mathematics at the University of Iceland (UI) seeks applicants for a fully funded 3 year PhD position for the project Statistical Models for Glaciology.

The student will develop Bayesian hierarchical spatio-temporal models to the field of glaciology, working with a consortium of experts at the University of Iceland, the University of Missouri and the Norwegian University of Science and Technology. The key people in the consortium are Prof. Birgir Hrafnkelsson at UI, Prof. Chris Wikle, and Prof. Håvard Rue, experts in spatial statistics and Bayesian computation. Another key person is Prof. Gudfinna Adalgeirsdottir at UI, an expect in glaciology. The Glaciology group at UI possesses extensive data and knowledge about the Icelandic glaciers.

The application deadline is February 29, 2016.

Detailed project description

Job ad with information on how to apply:

never mind the big data here’s the big models [workshop]

Maybe the last occurrence this year of the pastiche of the iconic LP of the Sex Pistols!, made by Tamara Polajnar. The last workshop as well of the big data year in Warwick, organised by the Warwick Data Science Institute. I appreciated the different talks this afternoon, but enjoyed particularly Dan Simpson’s and Rob Scheichl’s. The presentation by Dan was so hilarious that I could not resist asking him for permission to post the slides here:

Not only hilarious [and I have certainly missed 67% of the jokes], but quite deep about the meaning(s) of modelling and his views about getting around the most blatant issues. Ron presented a more computational talk on the ways to reach petaflops on current supercomputers, in connection with weather prediction models used (or soon to be used) by the Met office. For a prediction area of 1 km². Along with significant improvements resulting from multiscale Monte Carlo and quasi-Monte Carlo. Definitely impressive! And a brilliant conclusion to the Year of Big Data (and big models).

postdoc in the Alps

Post-doctoral Position in Spatial/Computational Statistics (Grenoble, France)

A post-doctoral position is available in Grenoble, France, to work on computational methods for spatial point process models. The candidate will work with Simon Barthelmé (GIPSA-lab, CNRS) and Jean-François Coeurjolly (Univ. Grenoble Alpes, Laboratory Jean Kuntzmann) on extending point process methodology to deal with large datasets involving multiple sources of variation. We will focus on eye movement data, a new and exciting application area for spatial statistics. The work will take place in the context of an interdisciplinary project on eye movement modelling involving psychologists, statisticians and applied mathematicians from three different institutes in Grenoble.

The ideal candidate has a background in spatial or computational statistics or machine learning. Knowledge of R (and in particular the package spatstat) and previous experience with point process models is a definite plus.

The duration of the contract is 12+6 months, starting 01.10.2015 at the earliest. Salary is according to standard CNRS scale (roughly EUR 2k/month).

Grenoble is the largest city in the French Alps, with a very strong science and technology cluster. It is a pleasant place to live, in an exceptional mountain environment.

Statistics for spatio-temporal data [book review]

Here is the new reference book about spatial and spatio-temporal statistical modelling!  Noel Cressie wrote the earlier classic Statistics for Spatial Data in 1993 and he has now co-authored with Christopher Wikle (a plenary speaker at ISBA 2014 in Cancún) the new bible on the topic. And with a very nice cover of a Guatemaltec lienzo about the Spanish conquest. (Disclaimer: as I am a good friend of Noel, do not expect this review to remain unbiased!)

“…we state the obvious, that political boundaries cannot hold back a one-meter rise in sea level; our environment is ultimately a global resource and its stewardship is an international responsibility.” (p.11)

The book is a sum (in the French/Latin meaning of somme/summa when applied to books—I am not sure this explanation makes any sense!) and, as its predecessor, it covers an enormous range of topics and methods. So do not expect a textbook coverage of most notions and prepare to read further articles referenced in the text. One of the many differences with the earlier book is that MCMC appears from the start as a stepping stone that is necessary to handle

“…there are model-selection criteria that could be invoked (e.g., AIC, BIC, DIC, etc.), which concentrate on the twin pillars of predictability and parsimony. But they do not address the third pillar, namely scientific interpretability (i.e., knowledge).” (p.33)

The first chapter of the book is actually a preface motivating the topics covered by the book, which may be confusing on a first read, esp. for a graduate student, as there is no math formula and no model introduced at this stage. Anyway, this is not really a book made for a linear read. It is quite  witty (with too many quotes to report here!) and often funny (I learned for instance that Einstein’s quote “Everything should be made as simple as possible, but not simpler” was a paraphrase of an earlier lecture, invented by the Reader’s Digest!).

“Thus, we believe that it is not helpful to try to classify probability distributions that determine the statistical models, as subjective or objective. Better questions to ask are about the sensitivity of inferences to model choices and whether such choices make sense scientifically.” (p.32)

The overall tone of the book is mostly Bayesian, in a non-conflictual conditional probability way, insisting on hierarchical (Bayesian) model building. Incidentally, it uses the same bracket notation for generic distributions (densities) as in Gelfand and Smith (JASA, 1990), i.e. [X|Y] and [X|Z,y][Z|y,θ], notation that did not get much of a fan club. (I actually do not know where it stemmed from.) The second chapter contains an illustration of the search for the USS Scorpion using a Bayesian model (including priors built from experts’ opinions), example which is also covered [without the maths!] in Sharon McGrayne’s Theory that would not die.

The book is too rich and my time is too tight (!) to cover each chapter in details.  (For instance, I am not so happy with the temporal chapter in that it moves away from the Bayesian perspective without much of a justification.) Suffice to say then that it appears like an updated and improved version of its predecessor, with 45 pages of references, some of them quite recent. If I was to teach from this book at a Master level, it would take the whole academic year and then some, assuming enough mathematical culture from the student audience.

As an addendum, I noticed several negative reviews on amazon due to the poor quality of the printing, but the copy I received from John Wiley was quite fine, with the many colour graphs well-rendered. Maybe an earlier printing or a different printing agreement?

LCM 2012, Trondheim (2)

Today was a much more rewarding day for me at LGM2012 as I was well-rested [and well-fed, thanks to a fantastic breakfast on brunost and gamalost cheeses!]. Indeed, I was able to follow almost all talks without micro-dozing intermissions and I particularly enjoyed those connected with ecology problems, incl. the one on musk oxen. Those morning talks also led me to ponder about the relevance of modelling the corresponding phenomena via Gaussian processes, beyond the computing advantages brought by the Gaussian nature of the underlying process and the availability of INLA. (One could rightly wonder what an agnostic like me is doing at an LGM conference: however, I find those new perspectives on spatial modelling and computing quite refreshing, as well as bringing me in touch with new communities.) Before getting any further into criticisms, I think I should read more carefully about those processes, for instance through Carl Rasmussen and Chris Willliams’  Gaussian Processes for Machine Learning… The afternoon saw more methodological talks with David Dunson speaking about Gaussian processes as a basis for Bayesian non-parametrics (and their frequentist convergence properties), which was quite interesting (although I wish David had given one talk instead of the equivalent to three talks!), and Chris Williams discussing the approximation techniques for Gaussian process regression,  another broad and informative talk! The poster session was also highly diverse, from many case studies to methodological entries on compound likelihood, Bayesian model choice and determinant fast computation. However, I did not stay till the end to go running along the beautiful coast of the Tromdheimsfjord, around the Lade peninsula, enjoying the sun that had showed up in the late afternoon if not the fierce and cold wind…

Incidentally, I learned today (during Dan Simpson’s reinvigorating talk) that Cholesky was a French mathematician (who died at the very end of WWI, at the age of 43, as a colonel in the French artillery, and whose famous decomposition got published posthumously by a fellow officer).