ISBA 2016 [#5]

On Thursday, I started the day by a rather masochist run to the nearby hills, not only because of the very hour but also because, by following rabbit trails that were not intended for my size, I ended up being scratched by thorns and bramble all over!, but also with neat views of the coast around Pula.  From there, it was all downhill [joke]. The first morning talk I attended was by Paul Fearnhead and about efficient change point estimation (which is an NP hard problem or close to). The method relies on dynamic programming [which reminded me of one of my earliest Pascal codes about optimising a dam debit]. From my spectator’s perspective, I wonder[ed] at easier models, from Lasso optimisation to spline modelling followed by testing equality between bits. Later that morning, James Scott delivered the first Bayarri Lecture, created in honour of our friend Susie who passed away between the previous ISBA meeting and this one. James gave an impressive coverage of regularisation through three complex models, with the [hopefully not degraded by my translation] message that we should [as Bayesians] focus on important parts of those models and use non-Bayesian tools like regularisation. I can understand the practical constraints for doing so, but optimisation leads us away from a Bayesian handling of inference problems, by removing the ascertainment of uncertainty…

Later in the afternoon, I took part in the Bayesian foundations session, discussing the shortcomings of the Bayes factor and suggesting the use of mixtures instead. With rebuttals from [friends in] the audience!

This session also included a talk by Victor Peña and Jim Berger analysing and answering the recent criticisms of the Likelihood principle. I am not sure this answer will convince the critics, but I won’t comment further as I now see the debate as resulting from a vague notion of inference in Birnbaum‘s expression of the principle. Jan Hannig gave another foundation talk introducing fiducial distributions (a.k.a., Fisher’s Bayesian mimicry) but failing to provide a foundational argument for replacing Bayesian modelling. (Obviously, I am definitely prejudiced in this regard.)

The last session of the day was sponsored by BayesComp and saw talks by Natesh Pillai, Pierre Jacob, and Eric Xing. Natesh talked about his paper on accelerated MCMC recently published in JASA. Which surprisingly did not get discussed here, but would definitely deserve to be! As hopefully corrected within a few days, when I recoved from conference burnout!!! Pierre Jacob presented a work we are currently completing with Chris Holmes and Lawrence Murray on modularisation, inspired from the cut problem (as exposed by Plummer at MCMski IV in Chamonix). And Eric Xing spoke about embarrassingly parallel solutions, discussed a while ago here.