The Van Dantzig Seminar is a nationwide series of lectures in statistics, which features renowned international and local speakers, from the full width of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the "Theory of Collective Phenomena" (i.e. statistics) in Amsterdam. The seminar will convene 4 to 6 times a year at varying locations, and is supported financially by among others the STAR cluster and the Section Mathematical Statistics of the VVS-OR.
Speakers
Christian Robert (Paris-Dauphine)
Evidence estimation in finite and infinite mixture models and applications
Joint work with Adrien Hairault (Paris Dauphine) and Judith Rousseau (Oxford)
Estimating the model evidence - or mariinal likelihood of the data - is a notoriously difficult task for finite and infinite mixture models and we reexamine here different Monte Carlo techniques advocated in the recent literature, as well as novel approaches based on Geyer (1994) reverse logistic regression technique, Chib (1995) algorithm, and Sequential Monte Carlo (SMC). Applications are numerous. In particular, testing for the number of components in a finite mixture model or against the fit of a finite mixture model for a given dataset has long been and still is an issue of much interest, albeit yet missing a fully satisfactory resolution. Using a Bayes factor to find the right number of components K in a finite mixture model is known to provide a consistent procedure. We furthermore establish the consistence of the Bayes factor when comparing a parametric family of finite mixtures against the nonparametric 'strongly identifiable' Dirichlet Process Mixture (DPM) model.
François Caron (Oxford)
Sparse graphs based on exchangeable random measures: properties, models and examples
Random simple and multigraph models based on exchangeable random measures, aka graphex processes or generalised graphon models, have recently been proposed as a versatile class of sparse random graph models. This class of models can be seen as a generalisation of the popular graphon models. I will present this class of models, discuss some of their asymptotic properties (degree distribution, clustering coefficients). I will also present some particular models with interpretable parameters within this class and their use for discovering latent communities in sparse real-world networks.
After the lectures, drinks will be served.
