  We propose a novel approach to parameter estimation for simulator-based
statistical models with intractable likelihood. Our proposed method involves
recursive application of kernel ABC and kernel herding to the same observed
data. We provide a theoretical explanation regarding why the approach works,
showing (for the population setting) that, under a certain assumption, point
estimates obtained with this method converge to the true parameter, as
recursion proceeds. We have conducted a variety of numerical experiments,
including parameter estimation for a real-world pedestrian flow simulator, and
show that in most cases our method outperforms existing approaches.
