A Scheme for Adaptive Selection of Population Sizes in Approximate Bayesian Computation - Sequential Monte Carlo

Abstract

Parameter inference and model selection in systems biology often requires likelihood-free methods, such as Approximate Bayesian Computation (ABC). In recent years, this approach has frequently been combined with a Sequential Monte Carlo (ABC-SMC) scheme. In this scheme, the approximation of the posterior distribution through a population of particles is iteratively improved by a sequential sampling strategy. However, it has been difficult to give general guidelines on how to choose the size of these populations. In this manuscript, we propose a method to adaptively and automatically select these population sizes. The method exploits the cross-validated approximation error of a kernel density estimate of the particles in the current population to select the number of particles for the subsequent population.