Big Bayes goes South

At the Big [Data] Bayes conference this week [which I found quite exciting despite a few last minute cancellations by speakers] there were a lot of clustering talks including the ones by Amy Herring (Duke), using a notion of centering that should soon appear on arXiv. By Peter Müller (UT, Austin) towards handling large datasets. Based on a predictive recursion that takes one value at a time, unsurprisingly similar to the update of Dirichlet process mixtures. (Inspired by a 1998 paper by Michael Newton and co-authors.) The recursion doubles in size at each observation, requiring culling of negligible components. Order matters? Links with Malsiner-Walli et al. (2017) mixtures of mixtures. Also talks by Antonio Lijoi and Igor Pruenster (Boconni Milano) on completely random measures that are used in creating clusters. And by Sylvia Frühwirth-Schnatter (WU Wien) on creating clusters for the Austrian labor market of the impact of company closure. And by Gregor Kastner (WU Wien) on multivariate factor stochastic models, with a video of a large covariance matrix evolving over time and catching economic crises. And by David Dunson (Duke) on distance clustering. Reflecting like myself on the definitely ill-defined nature of the [clustering] object. As the sample size increases, spurious clusters appear. (Which reminded me of a disagreement I had had with David McKay at an ICMS conference on mixtures twenty years ago.) Making me realise I missed the recent JASA paper by Miller and Dunson on that perspective.

Some further snapshots (with short comments visible by hovering on the picture) of a very high quality meeting [says one of the organisers!]. Following suggestions from several participants, it would be great to hold another meeting at CIRM in a near future.

þe Norse farce beamer style

short course on MCMC at CiRM [slides]

Here are the [recycled] slides for the introductory lecture I gave this morning at CIRM, with the side information that it appears Slideshare has gone to another of these stages when slides cannot be played on this blog [when using Firefox]…

back to Wales [54th Gregynog Statistical Conference]

Today, provided the Air France strike let me fly to Birmingham airport!, I am back at Gregynog Hall, Wales, for the weekend conference organised there every year by some Welsh and English statistics departments, including Warwick. Looking forward to the relaxed gathering in the glorious Welsh countryside (and hoping that my knee will have sufficiently recovered for some trail running around Gregynog Hall…!) Here are the slides of the talk I will present tomorrow:

against the Dutch book argument

In continuation of the previous post, here are my slides for this afternoon talk at the 4th BFF conference (with nothing against Dutch people of course!, or anyone actually since this is a “best friends forever” conference):

RSS Read Paper

I had not attended a Read Paper session at the Royal Statistical Society in Errol Street for quite a while and hence it was quite a treat to be back there, especially as a seconder of the vote of thanks for the paper of Andrew Gelman and Christian Hennig. (I realised at this occasion that I had always been invited as a seconder, who in the tradition of the Read Papers is expected to be more critical of the paper. When I mentioned that to a friend, he replied they knew me well!) Listening to Andrew (with no slide) and Christian made me think further about the foundations of statistics and the reasons why we proceed as we do. In particular about the meaning and usages of a statistical model. Which is only useful (in the all models are wrong meme) if the purpose of the statistical analysis is completely defined. Searching for the truth does not sound good enough. And this brings us back full circle to decision theory in my opinion, which should be part of the whole picture and the virtues of openness, transparency and communication.

During his talk, Christian mentioned outliers as a delicate issue in modelling and I found this was a great example of a notion with no objective meaning, in that it is only defined in terms of or against a model, in that it addresses the case of observations not fitting a model instead of a model not fitting some observations, hence as much a case of incomplete (lazy?) modelling as an issue of difficult inference. And a discussant (whose Flemish name I alas do not remember) came with the slide below of an etymological reminder that originally (as in Aristotle) the meaning of objectivity and subjectivity were inverted, in that the later meant about the intrinsic nature of the object, while the former was about the perception of this object. It is only in the modern (?) era that Immanuel Kant reverted the meanings…Last thing, I plan to arXiv my discussions, so feel free to send me yours to add to the arXiv document. And make sure to spread the word about this discussion paper to all O-Bayesians as they should feel concerned about this debate!

MCqMC 2016 [#1]

This week, I attend the MCqMC 2016 conference in Stanford, which is quite an exciting gathering of researchers involved in various aspects of Monte Carlo methods. As Art Owen put it in his welcoming talk, the whole Carlo family is there! (Not to mention how pleasant the Stanford Campus currently is, after the scorching heat we met the past week in Northern California inlands.) My talk is on folded Markov chains, which is a proposal Randal and I have been working on for quite a while, with Gareth joining us more recently. The basic idea was inspired from a discussion I had about a blog post, so long ago that I cannot even trace it! Namely, when defining an inside set A and an outside set, such that the outside set can be projected onto the inside set, one can fold both the target and the proposal, essentially looking at a collection of values for each step of the Markov chain. In other words, the problem can be reduced to A at essentially no cost and with the benefits of a compact support A and of a possibly uniformly ergodic Markov chain. We are still working on the paper, but the idea is both cool and straightforward, so we decided to talk about it at Nordstat 2016 and now MCqMC 2016.