Archive for ABC-Gibbs

my talk in Newcastle

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , on November 13, 2020 by xi'an

I will be talking (or rather zooming) at the statistics seminar at the University of Newcastle this afternoon on the paper Component-wise approximate Bayesian computation via Gibbs-like steps that just got accepted by Biometrika (yay!). Sadly not been there for real, as I would have definitely enjoyed reuniting with friends and visiting again this multi-layered city after discovering it for the RSS meeting of 2013, which I attended along with Jim Hobert and where I re-discussed the re-Read DIC paper. Before traveling south to Warwick to start my new appointment there. (I started with a picture of Seoul taken from the slopes of Gwanaksan about a year ago as a reminder of how much had happened or failed to happen over the past year…Writing 2019 as the year was unintentional but reflected as well on the distortion of time induced by the lockdowns!)

 

my demonic talk

Posted in Statistics with tags , , , , , , , , , , , on May 13, 2020 by xi'an

in Bristol for the day

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on February 28, 2020 by xi'an

I am in Bristol for the day, giving a seminar at the Department of Statistics where I had not been for quite a while (and not since the Department has moved to a beautifully renovated building). The talk is on ABC-Gibbs, whose revision is on the verge of being resubmitted. (I also hope Greta will let me board my plane tonight…)

off to Vancouver

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on December 7, 2019 by xi'an

Today I am flying to Vancouver for an ABC workshop, the second Symposium on Advances in Approximate Bayesian Inference, which is a pre-NeurIPS workshop following five earlier editions, to some of which I took part. With an intense and exciting programme. Not attending the following NeurIPS as I had not submitted any paper (and was not considering relying on a lottery!). Instead, I will give a talk at ABC UBC on Monday 4pm, as, coincidence, coincidence!, I was independently invited by UBC to the IAM-PIMS Distinguished Colloquium series. Speaking on ABC on a broader scale than in the workshop. Where I will focus on ABC-Gibbs. (With alas no time for climbing, missing an opportunity for a winter attempt at The Stawamus Chief!)

ABC-SAEM

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , , , on October 8, 2019 by xi'an

In connection with the recent PhD thesis defence of Juliette Chevallier, in which I took a somewhat virtual part for being physically in Warwick, I read a paper she wrote with Stéphanie Allassonnière on stochastic approximation versions of the EM algorithm. Computing the MAP estimator can be done via some adapted for simulated annealing versions of EM, possibly using MCMC as for instance in the Monolix software and its MCMC-SAEM algorithm. Where SA stands sometimes for stochastic approximation and sometimes for simulated annealing, originally developed by Gilles Celeux and Jean Diebolt, then reframed by Marc Lavielle and Eric Moulines [friends and coauthors]. With an MCMC step because the simulation of the latent variables involves an untractable normalising constant. (Contrary to this paper, Umberto Picchini and Adeline Samson proposed in 2015 a genuine ABC version of this approach, paper that I thought I missed—although I now remember discussing it with Adeline at JSM in Seattle—, ABC is used as a substitute for the conditional distribution of the latent variables given data and parameter. To be used as a substitute for the Q step of the (SA)EM algorithm. One more approximation step and one more simulation step and we would reach a form of ABC-Gibbs!) In this version, there are very few assumptions made on the approximation sequence, except that it converges with the iteration index to the true distribution (for a fixed observed sample) if convergence of ABC-SAEM is to happen. The paper takes as an illustrative sequence a collection of tempered versions of the true conditionals, but this is quite formal as I cannot fathom a feasible simulation from the tempered version and not from the untempered one. It is thus much more a version of tempered SAEM than truly connected with ABC (although a genuine ABC-EM version could be envisioned).