“A more automated approach would be useful for lazy versions of ABC SMC algorithms.”
Dennis Prangle just arXived the work on lazy ABC he had presented in Oxford at the i-like workshop a few weeks ago. The idea behind the paper is to cut down massively on the generation of pseudo-samples that are “too far” from the observed sample. This is formalised through a stopping rule that puts the estimated likelihood to zero with a probability 1-α(θ,x) and otherwise divide the original ABC estimate by α(θ,x). Which makes the modification unbiased when compared with basic ABC. The efficiency appears when α(θ,x) can be computed much faster than producing the entire pseudo-sample and its distance to the observed sample. When considering an approximation to the asymptotic variance of this modification, Dennis derives a optimal (in the sense of the effective sample size) if formal version of the acceptance probability α(θ,x), conditional on the choice of a “decision statistic” φ(θ,x). And of an importance function g(θ). (I do not get his Remark 1 about the case when π(θ)/g(θ) only depends on φ(θ,x), since the later also depends on x. Unless one considers a multivariate φ which contains π(θ)/g(θ) itself as a component.) This approach requires to estimate
as a function of φ: I would have thought (non-parametric) logistic regression a good candidate towards this estimation, but Dennis is rather critical of this solution.
I added the quote above as I find it somewhat ironical: at this stage, to enjoy laziness, the algorithm has first to go through a massive calibration stage, from the selection of the subsample [to be simulated before computing the acceptance probability α(θ,x)] to the construction of the (somewhat mysterious) decision statistic φ(θ,x) to the estimation of the terms composing the optimal α(θ,x). The most natural choice of φ(θ,x) seems to be involving subsampling, still with a wide range of possibilities and ensuing efficiencies. (The choice found in the application is somehow anticlimactic in this respect.) In most ABC applications, I would suggest using a quick & dirty approximation of the distribution of the summary statistic.
A slight point of perplexity about this “lazy” proposal, namely the static role of ε, which is impractical because not set in stone… As discussed several times here, the tolerance is a function of many factors incl. all the calibration parameters of the lazy ABC, rather than an absolute quantity. The paper is rather terse on this issue (see Section 4.2.2). It seems to me that playing with a large collection of tolerances may be too costly in this setting.
Yesterday a arXival by Antoine Luciano, Charly Andral (both PhD students, now or then, at Paris Dauphine), Robin Ryder (formerly at Paris Dauphine, now at Imperial College London) and myself got posted. It proposes to improve the scalability of ABC methods by exploiting the (full or partial) exchangeability in the data by implementing permutation-based matching between observed and simulated samples. This significantly improves computational efficiency, which is further enhanced by sequential strategies such as over-sampling, which facilitates early-stage acceptance by temporarily increasing the number of simulated compartments, and under-matching, which relaxes the acceptance condition by matching only subsets of the data. The map of France appears in connection with an application of the method to estimating SIR parameters, department by department. (It is also reminding me of the cover of Markov Chain Monte Carlo methods in practice, the 1996 contributed book edited by Wally Gilks, Sylvia Richardson and David Spiegelhalter.)
Another 
