Xi'an's Og

One morning session on optimal transport after first-hand witnessing the impressive ballet of orderly lines entering the subway at Nagoya Station (and a very early run along the river and a high humidity rate, hence the picture of empty street at 5am). With Hugo Lavenant exhibiting optimal rates for Bayesian nonparametrics using Wasserstein distances, Pierre Jacob coupling MCMC chains, and Anya Katsevich investigating non-Gaussian asymptotic distributions in high dimensions (beyond Bernstein-von Mises). Then I tried to attend the session on Bayesian Uncertainty Quantification and Posterior Sampling for Large-Scale Generative Models, but it proved too popular for the number of seats, and I ended up discussing with others. After a nap related to my jetlag induced, early, rise I went back to chair Sid Chib’s Foundation Lecture, where he discussed the use of an orbit of models in Bayesian model choice, rather than the (MAP) most likely one. Based on their 2018 JASA paper which I already discussed in Paris with Anna Simoni presenting. Hence reminding me of points I presumably already made, from the issue of having too many models to realistically explore to constructing coherent priors across them, with the fractional, empirical, proxy of using a (same) fraction of the sample as a learning sample, to more philosophical issues like missing a utility function about having to chose a model, especially with all models being wrong, missing an uncertainty quantification on the evidence itself, rather than using most likely models (MAP!), called an orbit by Sid (which requires some calibration). Since the uncertainty represented by the sample induces an uncertainty in the ranking of models. And a most appropriate, almost local, occurrence of the Rashomon principle!!! And I finished the day mixing with many friends in the poster session¹, where Darren Wraith presented our ongoing work on novel, adaptive, importance, sampling.