I’ve just returned from two weeks in France, the first week on the International Statistical Ecology Conference 2014 in Montpellier, and the second at the Laboratoire d’Écologie Alpine (LECA) in Grenoble, visiting the groups of Wilfried Thuiller and Sébastien Lavergne, which was both great.
Some impressions from the ISEC:
- First of all, my compliments to the local organizers, who handled their job with enchanting Mediterranean charme.
- For the future: don’t be afraid to present at an ISEC if you’re not a hardcore statistician. The majority of people were stats users rather than developers or statisticians, and it was overall a very friendly crowd.
- Nearly everyone was using hierarchical Bayesian models.
- In general, the presentations confirmed one problem I see with the current practice of Bayesian inference, which is that it allows specifying and fitting very sophisticated models, but involves few checks of the assumptions of those models. I’m thinking about checking residuals, model selection, cross validation. All possible, but rarely done in practice, clearly also because of the computational burden. I see this as a certain problem, because what are all the advances in model specification worth if you can’t systematically validate them.
- There were surprisingly few talks on ABC and related topics. I would have expected that more people are working on that. But we had two great keynotes on simulation-based inference, one by Marc Beaumont on ABC, and one by Simon Wood on his synthetic likelihood approach. I have argued earlier that the parallels between them are not sufficiently recognized. Neither of the keynotes made an attempt to bridge this gap though, although Marc Beaumont made a few comments in this direction. Marc also presented some new ABC application together with Richard Sibly, using ABC to fit an earthworm model. I guess it’s this model but I’m not sure. Looked interesting.
- Perry de Valpine gave a great plenary in general. He also presented a new Bayesian modeling framework, NIMBLE, that uses the BUGS model specification, translates this in C++ similar to STAN, but offers the possibility to specify your own sampling / simulation algorithms. Hence, a kind of hybrid between a general programming language and a DAG model specification language. Seemed worth trying out.
- On the topic of new frameworks: quite a bit of fuzz about AD model builder. I had heard of it but haven’t used it yet. From what I understand, it does fast MLE inference for nonlinear or hierarchical models via Laplace approximation. People I talked to were very positive about it, but I am still a bit skeptical whether a Laplace approximation is stable for more complicated problems. Another thing to try out.
- Ben Bolker gave a keynote on statistical machismo, citing the discussion initiated by Brian McGill as well as his own recent post on statistical software over at Dynamic Ecology. In the beginning I thought this could get controversial, but then it went into all too familiar directions for my taste. At least, it got a good discussion going on why we, the editors and reviewers, are always pushing for new methods (not that this hasn’t been said before).
- And many other interesting talks I won’t be able to cover here, for example keynotes by Marti Anderson, Nicholas Gotelli and Chris Wikle, and a great number of other interesting talks.
I was very happy to have my own talk on inference in chaotic state space models right after Simon’s keynote that largely dealt with the same topic. The talk was motivated by the story around this comment that we sent to PNAS last year, but concentrates more on the underlying problem of estimating state-space models when the dynamics are chaotic. If you are interested, here are the slides