A reminder that the 2025 Objective Bayes (O’Bayes) Methodology Conference will take place in Athens, Greece, from June 8 to 12, 2025, which I will alas miss (for attending BayesComp the week after). Registration for the O’Bayes25 meeting is now open, with an increase on the 470€ fees in March. Speakers and discussants are available on the website and poster can be proposed till 28 February 28. The tutorials on Sunday will be given by Guido Consonni, Rianna de Heide, and Mark Stell (University of Warwick).
Archive for BayesComp 2025
O’Bayes 2025 της Αθήνας
Posted in pictures, Statistics, Travel, University life with tags Athens, Athens University of Economics and Business, BayesComp 2025, foundations of objective Bayesian methodology, Greece, National Technical University of Athens, O'Bayes 2025, registration, registration fees, Singapore, University of Warwick on January 18, 2025 by xi'anO’Bayes 2025 [με αθηναϊκό άρωμα]
Posted in Statistics with tags Athens, Athens University of Economics and Business, BayesComp 2025, conference, foundations of objective Bayesian methodology, Greece, ISBA, ISBA conference, National Technical University of Athens, National University Singapore, O'Bayes, O'Bayes 2022, O'Bayes 2025, objective Bayes, Parthenon, Renzo Piano, scientific committee, Singapore, Stavros Niarchos Foundation, tutorial on November 6, 2024 by xi'an
The next O’Bayes conference will take place in Athens, 8-12 June 2025. Registration is open, as well as a call for posters. As in (all?) earlier episodes of these Objective Bayes methodology conferences, speakers and discussants are invited by the scientific committee. And the first afternoon consists of tutorials on different aspects of Objective Bayes methodology. The conference will take place in the Stavros Niarchos Foundation Cultural Centre. (Unfortunately, I will not be able to attend O’Bayes 2025, once again, due to my attending BayesComp 2025 the week after.)
BayesComp²⁵ in Singapore
Posted in pictures, Statistics, Travel with tags Arctic, BayesComp 2025, Bayesian computing, conference, equator, ISBA, Levi, National University Singapore, NUS, Singapore on August 28, 2023 by xi'an
BayesComp²³ [aka MCMski⁶]
Posted in Books, Mountains, pictures, Running, Statistics, Travel, University life with tags 0.234, ABC, agent-based models, BayesComp 2025, Bayesian GANs, Bernstein-von Mises theorem, Hokkaido, maximum mean discrepancy, Milano, misspecification, noise contrasting estimation, posters, qMC, quadrature, Rademacher complexity, score function, simulated annealing, SMC, Steve Fienberg, Wasserstein distance on March 20, 2023 by xi'an
The main BayesComp meeting started right after the ABC workshop and went on at a grueling pace, and offered a constant conundrum as to which of the four sessions to attend, the more when trying to enjoy some outdoor activity during the lunch breaks. My overall feeling is that it went on too fast, too quickly! Here are some quick and haphazard notes from some of the talks I attended, as for instance the practical parallelisation of an SMC algorithm by Adrien Corenflos, the advances made by Giacommo Zanella on using Bayesian asymptotics to assess robustness of Gibbs samplers to the dimension of the data (although with no assessment of the ensuing time requirements), a nice session on simulated annealing, from black holes to Alps (if the wrong mountain chain for Levi), and the central role of contrastive learning à la Geyer (1994) in the GAN talks of Veronika Rockova and Éric Moulines. Victor Elvira delivered an enthusiastic talk on our massively recycled importance on-going project that we need to complete asap!
While their earlier arXived paper was on my reading list, I was quite excited by Nicolas Chopin’s (along with Mathieu Gerber) work on some quadrature stabilisation that is not QMC (but not too far either), with stratification over the unit cube (after a possible reparameterisation) requiring more evaluations, plus a sort of pulled-by-its-own-bootstrap control variate, but beating regular Monte Carlo in terms of convergence rate and practical precision (if accepting a large simulation budget from the start). A difficulty common to all (?) stratification proposals is that it does not readily applies to highly concentrated functions.
I chaired the lightning talks session, which were 3mn one-slide snapshots about some incoming posters selected by the scientific committee. While I appreciated the entry into the poster session, the more because it was quite crowded and busy, if full of interesting results, and enjoyed the slide solely made of “0.234”, I regret that not all poster presenters were not given the same opportunity (although I am unclear about which format would have permitted this) and that it did not attract more attendees as it took place in parallel with other sessions.
In a not-solely-ABC session, I appreciated Sirio Legramanti speaking on comparing different distance measures via Rademacher complexity, highlighting that some distances are not robust, incl. for instance some (all?) Wasserstein distances that are not defined for heavy tailed distributions like the Cauchy distribution. And using the mean as a summary statistic in such heavy tail settings comes as an issue, since the distance between simulated and observed means does not decrease in variance with the sample size, with the practical difficulty that the problem is hard to detect on real (misspecified) data since the true distribution behing (if any) is unknown. Would that imply that only intrinsic distances like maximum mean discrepancy or Kolmogorov-Smirnov are the only reasonable choices in misspecified settings?! While, in the ABC session, Jeremiah went back to this role of distances for generalised Bayesian inference, replacing likelihood by scoring rule, and requirement for Monte Carlo approximation (but is approximating an approximation that a terrible thing?!). I also discussed briefly with Alejandra Avalos on her use of pseudo-likelihoods in Ising models, which, while not the original model, is nonetheless a model and therefore to taken as such rather than as approximation.
I also enjoyed Gregor Kastner’s work on Bayesian prediction for a city (Milano) planning agent-based model relying on cell phone activities, which reminded me at a superficial level of a similar exploitation of cell usage in an attraction park in Singapore Steve Fienberg told me about during his last sabbatical in Paris.
In conclusion, an exciting meeting that should have stretched a whole week (or taken place in a less congenial environment!). The call for organising BayesComp 2025 is still open, by the way.
