ABC in Helsinki (a.k.a. ABCruise) has started! With a terrific weather most adequate for a cruise on the Baltic. The ship on which the workshop takes place is certainly larger than any I have been on, including the Channel ferries, and the inside alley looks rather like a shopping centre! However, the setting is exceptional, with comfy sea-facing cabins and pleasant breaks (including fancy tea!) Plus, we have a quiet and cosy conference room that makes one forgets one is on a boat. Until it starts rocking. Or listing! The cruise boat is definitely large enough to be fairly stable. A unique experience we could consider for future (AB-see) workshops (with the caveat that we benefited from exceptional circumstances that brought the costs down to ridiculous amounts).

Richard Everitt talked about the synthetic likelihood approach and its connection with ABC. Making clear for me a point I had somewhat forgotten, namely that the approximative likelihood is a Gaussian at the observed summary statistics, but one centred at empirical moments derived from the simulation of pseudo summaries based on a given value of the parameter θ. So it is not an exact approach in that it does not converge to the true likelihood as the number of simulation grows to infinity. (While a kernel would converge.) That means it may (will) misrepresent the tails unless the distribution of the summary statistic is close to Normal. Richard also introduced bootstrap or bags of little bootstraps in order to speed up the generation of the pseudo-data, which makes sense albeit it moves the sampling away from the true model since it is conditional on a single simulation.

Jean-Michel Marin introduced the ABC inference algorithm we are currently working on, using regression random forests that differ from the classification forests we used for model selection. (The paper is close to completion so I hope to be able to tell more in a near future!) Clara Grazian presented her semi-parametric work using ABC with Brunero Liseo. That was part of her thesis. Thomas Schön presented an extension of his particle Gibbs with adaptive sampling to the case of degenerate transitions, using an ABC approximation to get around this central problem. A very interesting entry that I need to study deeper. And Caroline Colijn talked about ABC for trees, mostly about the selection of summary statistics towards comparing tree topologies, with a specific distance between trees that caters to the topology and only the topology.