After a position in Bristol advertised a few days ago, I want to point out there also is a position opening in Oxford, Department of Statistics, in conjunction with a fellowship from University College. The deadline is August 26, 2016, and applicants should contact Professor Arnaud Doucet for details.
Archive for University of Oxford
Another postdoctoral offer:
Richard Everitt organises an afternoon workshop on Bayesian computation in Reading, UK, on April 19, the day before the Estimating Constant workshop in Warwick, following a successful afternoon last year. Here is the programme:
1230-1315 Antonietta Mira, Università della Svizzera italiana 1315-1345 Ingmar Schuster, Université Paris-Dauphine 1345-1415 Francois-Xavier Briol, University of Warwick 1415-1445 Jack Baker, University of Lancaster 1445-1515 Alexander Mihailov, University of Reading 1515-1545 Coffee break 1545-1630 Arnaud Doucet, University of Oxford 1630-1700 Philip Maybank, University of Reading 1700-1730 Elske van der Vaart, University of Reading 1730-1800 Reham Badawy, Aston University 1815-late Pub and food (SCR, UoR campus)
and the general abstract:
The Bayesian approach to statistical inference has seen major successes in the past twenty years, finding application in many areas of science, engineering, finance and elsewhere. The main drivers of these successes were developments in Monte Carlo methods and the wide availability of desktop computers. More recently, the use of standard Monte Carlo methods has become infeasible due the size and complexity of data now available. This has been countered by the development of next-generation Monte Carlo techniques, which are the topic of this meeting.
The meeting takes place in the Nike Lecture Theatre, Agriculture Building [building number 59].
“The world is full of obvious things which nobody by any chance ever observes.” The Hound of the Baskervilles
In connection with the incoming publication of James Watson’s and Chris Holmes’ Approximating models and robust decisions in Statistical Science, Judith Rousseau and I wrote a discussion on the paper that has been arXived yesterday.
“Overall, we consider that the calibration of the Kullback-Leibler divergence remains an open problem.” (p.18)
While the paper connects with earlier ones by Chris and coauthors, and possibly despite the overall critical tone of the comments!, I really appreciate the renewed interest in robustness advocated in this paper. I was going to write Bayesian robustness but to differ from the perspective adopted in the 90’s where robustness was mostly about the prior, I would say this is rather a Bayesian approach to model robustness from a decisional perspective. With definitive innovations like considering the impact of posterior uncertainty over the decision space, uncertainty being defined e.g. in terms of Kullback-Leibler neighbourhoods. Or with a Dirichlet process distribution on the posterior. This may step out of the standard Bayesian approach but it remains of definite interest! (And note that this discussion of ours [reluctantly!] refrained from capitalising on the names of the authors to build easy puns linked with the most Bayesian of all detectives!)
Following the opening of the new Department of Statistics building in Oxford [which somewhat ironically is the former Department of Mathematics!], a professional photographer was commissioned for a photo cover of this move. Which is incidentally fantastic for the cohesion and work quality of the department, when compared with the former configuration in two disconnected buildings on South Parks Road. Not mentioning the vis-à-vis with Eagle and Child.
As the photographer happened to be there the very day I was teaching my Bayesian module for the OxWaSP PhD students, I ended up in some of the photographs (with no clear memory of this photographer, who was most unintrusive). With my Racoon River Brewing Co. tee-shirt I brought back from Des Moines. And was wearing in a very indirect allusion to the US primaries the night before!