Archive for 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).
The slides are directly extracted from the paper but it still took me quite a while to translate the paper into those, during the early hours of our Czech break this week.
One added perk of travelling to Nice is the flight there, as it parallels the entire French Alps, a terrific view in nice weather!
I just learned today that about 300 bouquetins had been killed in the French Alps the past few days as an hasty and ungrounded measure against bovine brucellosis. I find it amazing that the local authorities can act with so little scientific justification and against European regulations that make bouquetins a protected species. In comparison, the proposed culling of badgers in England went through experimental steps with some modicus of science. (Although it is supposed to resume next week despite Gareth’s recent ABC paper demonstrating culling is ineffective against bovine TB.)
My friends Luke Bornn, Natesh Pillai and Dawn Woodard just arXived along with Aaron Smith a short note on the convergence properties of ABC. When compared with acceptance-rejection or regular MCMC. Unsurprisingly, ABC does worse in both cases. What is central to this note is that ABC can be (re)interpreted as a pseudo-marginal method where the data comparison step acts like an unbiased estimator of the true ABC target (not of the original ABC target, mind!). From there, it is mostly an application of Christophe Andrieu’s and Matti Vihola’s results in this setup. The authors also argue that using a single pseudo-data simulation per parameter value is the optimal strategy (as compared with using several), when considering asymptotic variance. This makes sense in terms of simulating in a larger dimensional space but what of the cost of producing those pseudo-datasets against the cost of producing a new parameter? There are a few (rare) cases where the datasets are much cheaper to produce.