Archive for SFDS

ateliers statistiques bayésiens

Posted in Statistics with tags , , , , , , , on July 18, 2019 by xi'an

The French Statistical Association is running a training workshop on practical computational Bayesian methods on 10-12 September 2019 in Paris (IHP), animated by Sylvain LE CORFF (Telecom SudParis – Institut Polytechnique de Paris) for the initiation to « rstan », by Matthieu AUTHIER (Université de La Rochelle).

non-uniform Laplace generation

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , on June 5, 2019 by xi'an

This year, the French Statistical Society (SFDS) Prix Laplace has been granted to Luc Devroye, author of the Non-Uniform Random Generation bible. among many achievements!, prize that he will receive during the 2019 meeting in Nancy, this very week.

graphe, graphons, graphez !

Posted in Books, pictures, Statistics, University life with tags , , , , , , on December 3, 2018 by xi'an

clustering dynamical networks

Posted in pictures, Statistics, University life with tags , , , , , , , , , , on June 5, 2018 by xi'an


Yesterday I attended a presentation by Catherine Matias on dynamic graph structures, as she was giving a plenary talk at the 50th French statistical meeting, conveniently located a few blocks away from my office at ENSAE-CREST. In the nicely futuristic buildings of the EDF campus, which are supposed to represent cogs according to the architect, but which remind me more of these gas holders so common in the UK, at least in the past! (The E of EDF stands for electricity, but the original public company handled both gas and electricity.) This was primarily a survey of the field, which is much more diverse and multifaceted than I realised, even though I saw some recent developments by Antonietta Mira and her co-authors, as well as refereed a thesis on temporal networks at Ca’Foscari by Matteo Iacopini, which defence I will attend in early July. The difficulty in the approaches covered by Catherine stands with the amount and complexity of the latent variables induced by the models superimposed on the data. In her paper with Christophe Ambroise, she followed a variational EM approach. From the spectator perspective that is mine, I wondered at using ABC instead, which is presumably costly when the data size grows in space or in time. And at using tensor structures as in Mateo’s thesis. This reminded me as well of Luke Bornn’s modelling of basketball games following each player in real time throughout the game. (Which does not prevent the existence of latent variables.) But more vaguely and speculatively I also wonder at the meaning of the chosen models, which try to represent “everything” in the observed process, which seems doomed from the start given the heterogeneity of the data. While reaching my Keynesian pessimistic low- point, which happens rather quickly!, one could hope for projection techniques, towards reducing the dimension of the data of interest and of the parameter required by the model.

Statlearn17, Lyon

Posted in Kids, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , on April 6, 2017 by xi'an

Today and tomorrow, I am attending the Statlearn17 conference in Lyon, France. Which is a workshop with one-hour talks on statistics and machine learning. And which makes for the second workshop on machine learning in two weeks! Yesterday there were two tutorials in R, but I only took the train to Lyon this morning: it will be a pleasant opportunity to run tomorrow through a city I have not truly ever visited, if X’ed so many times driving to the Alps. Interestingly, the trip started in Paris with me sitting in the train next to another speaker at the conference, despite having switched seat and carriage with another passenger! Speaker whom I did not know beforehand and could only identify him by his running R codes at 300km/h.

Marc Yor

Posted in Books, Statistics, University life with tags , , , , , , on May 14, 2015 by xi'an

MarcYorMarcYor2

introduction à la Statistique, by Cédric Villani

Posted in Books, Kids, Statistics, University life with tags , , , , , , on January 26, 2014 by xi'an

crossing Rue Soufflot on my way to IHP from Vieux Campeur, March 28, 2013On Tuesday, there was a series of talks (in French) celebrating Statistics, with an introduction by Cédric Villani. (The talks are reproduced on the French Statistical Society (SFDS) webpage.) Rather unpredictably (!), Villani starts from an early 20th Century physics experiment leading to the estimation of the Avogadro constant from a series of integers. (Repeating an earlier confusion of his, he substitutes the probability of observing a rare event under the null with the probability of the alternative on the Higgs boson to be true!) A special mention to/of Francis Galton’s “supreme law of unreason”. And of surveys, pointing out the wide variability of a result for standard survey populations. But missing the averaging and more statistical effect of accumulating surveys, a principle at the core of Nate Silver‘s predictions. A few words again about the Séralini et al. experiments on Monsanto genetically modified maize NK603, attacked for their lack of statistical foundations. And then, hear hear!, much more than a mere mention of phylogenetic inference, with explanations about inverse inference, Markov Chain Monte Carlo algorithms on trees, convergence of Metropolis algorithms by Persi Diaconis, and Bayesian computations! Of course, this could be seen more as numerical probability than as truly statistics, but it is still pleasant to hear.

The last part of the talk more predictably links Villani’s own field of optimal transportation (which I would translate as a copula problem…) and statistics, mostly understood as empirical distributions. I find it somewhat funny that Sanov’s theorem is deemed therein to be a (or even the) Statistics theorem! I wonder how many statisticians could state this theorem… The same remark applies for the Donsker-Varadhan theory of large deviations. Still, the very final inequality linking the three types of information concepts is just… beautiful! You may spot in the last minute a micro confusion in repeating twice the definition for Fisher’s information rather than deducing that the information associated with a location family is constant. (And a no-so-necessary mention of the Cramer-Rao bound on unbiased estimators. Which could have been quoted as the Fréchet-Darmois-Cramer-Rao bound in such historical grounds ) A pleasant moment, all in all! (There are five other talks on that page, including one by Emmanuel Candés.)