Elisabeth Gassiat, a path in modern statistics [conférence]

Next Spring (31 May-02 June 2023), there will be a conference at Institut de mathématique d’Orsay in honour of our friend Elisabeth Gassiat and her contributions to statistics over the past 35 years, with Emmanuel Candès (Stanford University), Alexandra Carpentier (Universität Potsdam), Fabrice Gamboa (Université Toulouse III-Paul Sabatier), Alice Guionnet (ENS Lyon), Ramon van Handel (Princeton University), Thi Thu Huong Hoang (EDF), Eric Moulines (École Polytechnique), Richard Nickl (University of Cambridge), Judith Rousseau (University of Oxford / Université Paris Dauphine – PSL), Adeline Samson (Université Grenoble Alpes), Jean-Christophe Thalabard (Université Paris Cité), and Aad van der Vaart (TU Delft) as invited speakers. Registration is free and compulsory!

EM degeneracy

At the MHC 2021 conference today (to which I biked to attend for real!, first time since BayesComp!) I listened to Christophe Biernacki exposing the dangers of EM applied to mixtures in the presence of missing data, namely that the algorithm has a rising probability to reach a degenerate solution, namely a single observation component. Rising in the proportion of missing data. This is not hugely surprising as there is a real (global) mode at this solution. If one observation components are prohibited, they should not be accepted in the EM update. Just as in Bayesian analyses with improper priors, the likelihood should bar single or double  observations components… Which of course makes EM harder to implement. Or not?! MCEM, SEM and Gibbs are obviously straightforward to modify in this case.

Judith Rousseau also gave a fascinating talk on the properties of non-parametric mixtures, from a surprisingly light set of conditions for identifiability to posterior consistency . With an interesting use of several priors simultaneously that is a particular case of the cut models. Namely a correct joint distribution that cannot be a posterior, although this does not impact simulation issues. And a nice trick turning a hidden Markov chain into a fully finite hidden Markov chain as it is sufficient to recover a Bernstein von Mises asymptotic. If inefficient. Sylvain LeCorff presented a pseudo-marginal sequential sampler for smoothing, when the transition densities are replaced by unbiased estimators. With connection with approximate Bayesian computation smoothing. This proves harder than I first imagined because of the backward-sampling operations…

news from ISBA

Some news and reminders from the latest ISBA Bulletin (which also contains an obituary of Don Fraser by Christian Genest):

• Remember that the registration for ISBA 2021 is free till 1 May! The conference is fully online, from 28 June to 2 July
• the Bayesian young statisticians meeting BAYSM 21 will take place online, 1-3 September
• the useR! 2021 conference will also take place online, July 5-9
  
• the MHC2021 (Mixtures, Hidden Markov models, Clustering) conference will take place physically and online at Orsay, France, 2-4 June

MHC2020

There is a conference on mixtures (M) and hidden Markov models (H) and clustering (C) taking place in Orsay on June 17-19, next year. Registration is free if compulsory. With about twenty confirmed speakers. (Irrelevant as the following remark is, this is the opportunity to recall the conference on mixtures I organised in Aussois 25 years before! Which website is amazingly still alive at Duke, thanks to Mike West, my co-organiser along with Kathryn Roeder and Gilles Celeux. When checking the abstracts, I found only two presenters common to both conferences, Christophe Biernaki and Jiahua Chen. And alas several names of departed friends.)

Institut de Mathématique d’Orsay [jatp]