Archive for Venezia

advances in Bayesian modelling a Venezia

Posted in Statistics with tags , , , , , , , , , on July 4, 2018 by xi'an

snapshot from Venezia #3 [jatp]

Posted in Statistics with tags , , , , , , , on May 6, 2018 by xi'an

snapshot from Venezia #2 [jatp]

Posted in Kids, pictures, Travel with tags , , , , , , , , on May 5, 2018 by xi'an

snapshot from Venezia [jatp]

Posted in Statistics with tags , , , , , , , on April 29, 2018 by xi'an

slow food day

Posted in pictures, Travel with tags , , , , , on April 24, 2018 by xi'an


Yesterday, 21 April, was slow food day in Italy, and I took the opportunity of a visit to the Rialto fish market in Venezia to buy and cook some squids in their ink, which proved a fairly good dish. The other attempt at cooking Sant’Erasmo violet artichokes did not work so well, as I left too many of the outer leaves…

art brut

Posted in pictures, Running, Travel with tags , , , , on November 11, 2017 by xi'an

a Ca’Foscari [first Italian-French statistics seminar]

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on October 26, 2017 by xi'an

Apart from subjecting my [surprisingly large!] audience to three hours of ABC tutorial today, and after running Ponte della la Libertà to Mestre and back in a deep fog, I attended the second part of the 1st Italian-French statistics seminar at Ca’Foscari, Venetiarum Universitas, with talks by Stéfano Tonellato and Roberto Casarin. Stéfano discussed a most interesting if puzzling notion of clustering via Dirichlet process mixtures. Which indeed puzzles me for its dependence on the Dirichlet measure and on the potential for an unlimited number of clusters as the sample size increases. The method offers similarities with an approach from our 2000 JASA paper on running inference on mixtures without proper label switching, in that looking at pairs of allocated observations to clusters is revealing about the [true or pseudo-true] number of clusters. With divergence in using eigenvalues of Laplacians on similarity matrices. But because of the potential for the number of components to diverge I wonder at the robustness of the approach via non-parametric [Bayesian] modelling. Maybe my difficulty stands with the very notion of cluster, which I find poorly defined and mostly in the eyes of the beholder! And Roberto presented a recent work on SURE and VAR models, with a great graphical representation of the estimated connections between factors in a sparse graphical model.