ABC for vampires

Ritabrata Dutta (Warwick), along with coauthors including Anto Mira, published last week a paper in frontiers in physiology about using ABC for deriving the posterior distribution of the parameters of a dynamic blood (platelets) deposition model constructed by Bastien Chopard, the second author. While based on only five parameters, the model does not enjoy a closed form likelihood and even the simulation of a new platelet deposit takes about 10 minutes. The paper uses the simulated annealing ABC version, due to Albert, Künsch, and Scheidegger (2014), which relies a sequence of Metropolis kernels, associated with a decreasing sequence of tolerances, and claims better efficiency at reaching a stable solution. It also relies on the package abcpy, written by Ritabrata Dutta, in Python, for various aspects of ABC analysis. One feature of interest is the use of 24 summary statistics to conduct the inference on the 5 model parameters, a ratio of 24 to 5 that possibly gets improved by a variable selection tool such as random forests. Which would also avoid the choice of a specific loss function called the Bhattacharya distance (which sounds like entropy distance for the normal case).