running ABC when the likelihood is available

Today I refereed a paper where the authors used ABC to bypass convergence (and implementation) difficulties with their MCMC algorithm. And I am still pondering whether or not this strategy makes sense. If only because ABC needs to handle the same complexity and the same amount of parameters as an MCMC algorithm. While shooting “in the dark” by using the prior or a coarse substitute to the posterior. And I wonder at the relevance of simulating new data when the [true] likelihood value [at the observed data] can be computed. This would sound to me like the relevant and unique “statistics” worth considering…

rare events for ABC

Dennis Prangle, Richard G. Everitt and Theodore Kypraios just arXived a new paper on ABC, aiming at handling high dimensional data with latent variables, thanks to a cascading (or nested) approximation of the probability of a near coincidence between the observed data and the ABC simulated data. The approach amalgamates a rare event simulation method based on SMC, pseudo-marginal Metropolis-Hastings and of course ABC. The rare event is the near coincidence of the observed summary and of a simulated summary. This is so rare that regular ABC is forced to accept not so near coincidences. Especially as the dimension increases.  I mentioned nested above purposedly because I find that the rare event simulation method of Cérou et al. (2012) has a nested sampling flavour, in that each move of the particle system (in the sample space) is done according to a constrained MCMC move. Constraint derived from the distance between observed and simulated samples. Finding an efficient move of that kind may prove difficult or impossible. The authors opt for a slice sampler, proposed by Murray and Graham (2016), however they assume that the distribution of the latent variables is uniform over a unit hypercube, an assumption I do not fully understand. For the pseudo-marginal aspect, note that while the approach produces a better and faster evaluation of the likelihood, it remains an ABC likelihood and not the original likelihood. Because the estimate of the ABC likelihood is monotonic in the number of terms, a proposal can be terminated earlier without inducing a bias in the method.

This is certainly an innovative approach of clear interest and I hope we will discuss it at length at our BIRS ABC 15w5025 workshop next February. At this stage of light reading, I am slightly overwhelmed by the combination of so many computational techniques altogether towards a single algorithm. The authors argue there is very little calibration involved, but so many steps have to depend on as many configuration choices.

postdoc on missing data at École Polytechnique

Julie Josse contacted me for advertising a postdoc position at École Polytechnique, in Palaiseau, south of Paris. “The fellowship is focusing on missing data. Interested graduates should apply as early as possible since the position will be filled when a suitable candidate is found. The Centre for Applied Mathematics (CMAP) is  looking for highly motivated individuals able to develop a general multiple imputation method for multivariate continuous and categorical variables and its implementation in the free R software. The successful candidate will be part of research group in the statistical team on missing values. The postdoc will also have excellent opportunities to collaborate with researcher in public health with partners on the analysis of a large register from the Paris Hospital (APHP) to model the decisions and events when severe trauma patients are handled by emergency doctors. Candidates should contact Julie Josse at polytechnique.edu.”