Read System Collapse (Murderbot diaries #7) by Martha Wells. A sequel to the previous Network Effect, which I only partly remembered. The originaly of this volume is the unreliability of the half-organic android (on which I cannot expand because of spoilers!), even though the limited scope of the story is rather disappointing.
Made bagels at home with half rye, half wheat. Definitely tolerable (esp. when compared with my earlier experiment) and dense enough but open to improvements (I plan to try with no egg finish!, cooler raising temperature, and a different type of yeast).
Watched Korean Officer Black Belt (무도실무관)), thinking it was a series. Single movie it was, and definitely more than long enough as it aligns stereotypes and predictable battle scenes, with no active female character (but plenty of female victims)… And Japanese Golden Kamuy (ゴールデンカムイ) thinking it was a movie. Which it was except it ends unfinished (and is continued as a live-action television series). Inspired from a popular manga and taking place after the Russo-Japanese war of 1904. The manga inspiration is very clear, but the window on Ainu culture is worth noting, if somewhat shallow. (With the miso joke overstaying its limited comic potential.)
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!
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.
Here are some new teas I sampled this summer, when visiting a few tea shops here (Paris) and there (Montréal):
