Big Bayes postdoctoral position in Oxford [UK]

Forwarding a call for postdoctoral applications from Prof Judith Rousseau, with deadline 30 March:

Seeking a Postdoctoral Research Assistant, to join our group at the Department of Statistics. The Postdoctoral Research Assistant will be carrying out research for the ERC project General Theory for Big Bayes, reporting to Professor Judith Rousseau. They will provide guidance to junior members of the research group such as PhD students, and/or project volunteers.

The aim of this project is to develop a general theory for the analysis of Bayesian methods in complex and high (or infinite) dimensional models which will cover not only fine understanding of the posterior distributions but also an analysis of the output of the algorithms used to implement the approaches. The main objectives of the project are (briefly): 1) Asymptotic analysis of the posterior distribution of complex high dimensional models 2) Interactions between the asymptotic theory of high dimensional posterior distributions and computational complexity. We will also enrich these theoretical developments by 3) strongly related domains of applications, namely neuroscience, terrorism and crimes, and ecology.

The postholder will hold or be close to completion of a PhD/DPhil in statistics together with relevant experience. They will have the ability to manage own academic research and associated activities and have previous experience of contributing to publications/presentations. They will contribute ideas for new research projects and research income generation. Ideally, the postholder will also have experience in theoretical properties of Bayesian procedures and/or approximate Bayesian methods.

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

phishing alert at CIRM!

A strong and loud warning to all participants to one of the three events organised in CIRM, Luminy, Marseilles, in conjunction with Kerrie Mengersen’s Jean Morlet visiting chair, namely that some participants have received calls from crooks posting as CIRM admins, asking for credit card details towards supporting their stay at CIRM. This is a phishing attempt as self-supported participants to these events will be asked to pay at the end of their stay and never by phone or mail. In the meanwhile, there remains a few entries for both

1. Bayesian Statistics in the Big Data Era (26-30 Nov, 2018)
   
2. Young Bayesians and Big Data for Social Good (23-26 Nov., 2018)
   

for which registration is free but compulsory.

ABC in Montréal

There will be a symposium on ABC in Montréal this coming December, the day before NIPS, in a continuation of past years NIPS workshops. While invited speakers and panelists have been selected by the committee, a call for papers is open. Note that in continuation with the best “ABC in…” tradition, registration is free! I will unfortunately be unable to make it to this symposium, as the date clashes with our Big Bayes conference at CIRM (free registration, with still some places available!).