Archive for CIRM

CIRM in dire need of help

Posted in Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , , on November 24, 2020 by xi'an

The International Mathematics Conference Centre, CIRM, located in Luminy, Marseille, France, has been deeply disrupted by the COVID-19 pandemic. Up to 60 % of the events planned there have been cancelled so far and all remaining conferences have gone virtual or at best hybrid, open to a remote audience, including in an interactive way. This has required important and unexpected investments. Furthermore, this crisis happened at the worst possible moment as CIRM had just finished doubling its infrastructure capacities. This is why I strongly support the call from the French Mathematical Society (SMF) to make a donation to CIRM before the end of the year, especially from researchers who benefited from its hospitality and unique environment.

away from CIRM

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on November 5, 2020 by xi'an

Due to the new lockdown measures enforced in France and in particular in Marseilles, the CIRM workshop on QMC and randomness has turned virtual, and I will thus give my talk on Coordinate sampler : A non-reversible Gibbs-like sampler from Paris. Rather than from the Luminy campus after an early morning run to the top of Mont Puget as we used to do on the previous workshop there. With versions of PDMP running on QMC (which makes sense when considering the deterministic component of the sampler).

QMC at CIRM

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on October 21, 2020 by xi'an

non-reversible gerrymandering

Posted in Books, Statistics, Travel, University life with tags , , , , , , , on September 3, 2020 by xi'an

Gregory Herschlag, Jonathan C. Mattingly [whom I met in Oaxaca and who acknowledges helpful conversations with Manon Michel while at CIRM two years ago], Matthias Sachs, and Evan Wyse just posted an arXiv paper using non-reversible MCMC methods to improve sampling of voting district plans towards fighting (partisan) Gerrymandering. In doing so we extend thecurrent framework for construction of non-reversible Markov chains on discrete samplingspaces by considering a generalization of skew detailed balance. Since this means sampling in a discrete space, the method using lifting. Meaning adding a dichotomous dummy variable, “based on a notion of flowing the center of mass of districts along a defined vector field”. The paper is quite detailed about the validation and the implementation of the method. With this interesting illustration for the mixing properties of the different versions: