semi de Boulogne [1:29:33, 1243/8134, M5M 6/206, 8⁰+rain]

First time back to the Boulogne half-marathon since 2008! With clearly a much degraded time, albeit better than the previous race in Argentan. The route has changed, with a longer part in the Bois de Boulogne, sharing the road with the hordes of Sunday cyclists that pile up loops at high speed. But still a very fast one (with a record at 1:00:11 in 2013). The number has alas considerably increased since my last visit, with 9800 registrations, which makes running in the first kilometers a challenge with hidden sidewalks, parked cars and moppets, &tc. And a permanent difficulty in passing other runners, especially on a rainy day. (The only good side was being protected from headwinds.) Once on the road by the Seine River, I managed to pass a large group conglomerated around a (1:30) pace setter and moved at my own speed, till Km16 when I started to tire and realise I was alas missing some volume of training (as running in NYC was only a slow-paced jogging). Hence wasting about a minute on the final four kilometers… (Jogging back after the race to my car, parked 3km away, proved rather painful!) As the 1:30 time was my upper limit, I am still reasonably fine with the result (and the 4’14” per km) and hope I can train harder for the next race.

postdoc shortage

An interesting tribune in Nature (30 August) about the difficulty in hiring postdocs… I myself faced this difficulty in the recent years but though it was mostly due to unattractive French salaries and working conditions, or COVID issues (or myself!). Nature mentions politics, economics, ethics, and personal priorities as main reasons for the postdoctoral drought. In Britain, Brexit is definitely a central factor as candidates face enormous bills to secure entry to the United Kingdom (as Hai-Dang Dau, now in Oxford, was explaining to us after his successful PhD defense at ENSAE-CREST this morning). But more globally this may reflect a general exodus from academia towards company jobs, and their much more attractive salaries. Especially in STEM where Amazon and buddies created a new definition of “dream jobs”… Anyway,  I still have a prAirie postdoc position open in Paris Dauphine and the new PariSanté campus provides a great working environment, so feel free to contact me!

course PSL 2020

Irène Waldspurger, CNRS bronze medal

My colleague at Paris Dauphine, Irène Waldspurger, got one of the prestigious CNRS bronze medals this year. Irène is working on inverse problems and machine learning, with applications to sensing and imaging. Congrats!

ABC with Gibbs steps

With Grégoire Clarté, Robin Ryder and Julien Stoehr, all from Paris-Dauphine, we have just arXived a paper on the specifics of ABC-Gibbs, which is a version of ABC where the generic ABC accept-reject step is replaced by a sequence of n conditional ABC accept-reject steps, each aiming at an ABC version of a conditional distribution extracted from the joint and intractable target. Hence an ABC version of the standard Gibbs sampler. What makes it so special is that each conditional can (and should) be conditioning on a different statistic in order to decrease the dimension of this statistic, ideally down to the dimension of the corresponding component of the parameter. This successfully bypasses the curse of dimensionality but immediately meets with two difficulties. The first one is that the resulting sequence of conditionals is not coherent, since it is not a Gibbs sampler on the ABC target. The conditionals are thus incompatible and therefore convergence of the associated Markov chain becomes an issue. We produce sufficient conditions for the Gibbs sampler to converge to a stationary distribution using incompatible conditionals. The second problem is then that, provided it exists, the limiting and also intractable distribution does not enjoy a Bayesian interpretation, hence may fail to be justified from an inferential viewpoint. We however succeed in producing a version of ABC-Gibbs in a hierarchical model where the limiting distribution can be explicited and even better can be weighted towards recovering the original target. (At least with limiting zero tolerance.)