After another morning cycle of 2Xing Porte della Libertà (under a light and pleasant rain) and swimming in Sant’ Alviso (in too warm a water), I did not make it for the beginning of the Bayesian deep learning session, breakfast oblige!, and cumulated with different percolation events (ie, meeting friend after friend on my way to the classroom), I could not get enough of the session to report anything even barely useful!
As I did not rush fast enough to Andrew’s Foundation lecture (another sequence of percolations!), I had to stand in the back of the packed main amphitheatre (and former sorting hall of the Venice slaughterhouse!), Guido Cazzavillan’s Aula Magna, while he talked a fresco about some holes in Bayesian data analysis (the analysis, not the book!), those being [verbatim]
- the usual rules of conditional probability fail in the quantum realm,
- flat or weak priors lead to terrible inferences about things we care about,
- subjective priors are incoherent,
- Bayesian decision picks the wrong model,
- Bayes factors fail in the presence of flat or weak priors,
- for Cantorian reasons we need to check our models, but this destroys the coherence of Bayesian inference.
After lunch, I attended the (mostly sequential) simulation based inference (renamed from ABC!) session with a composite likelihood proposal by Lorenzo Rimella, that uses marginals to approximate the likelihood of a hidden Markov SIS epidemic model by composite likelihood towards getting more efficient if inexact versions. Then [1WABC webinar co-organiser] Umberto Picchini on surrogates for likelihood and posterior functions, with sequential improvements (w/o ABC and w/o neural networks). Called “Sequential mixture posterior and likelihood estimation”, using mixtures of experts when the weights are functions of the observed or simulated y. With adapting the number of components in the mixture. Comparing favourably with normalising flows. And Wentao Li on correcting by ABC for composite likelihood as in Ruli et al. (2016). Where a posterior distribution given composite scores (seen as [summary] statistics) is employed but requires a convergent estimator of the unknown parameter.
No congratulation today to our PhD student who managed to fall in a canal (but survived)..!
The next 
