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We propose a novel approach to parameter estimation for simulator-basedstatistical models with intractable likelihood. Our proposed method involvesrecursive application of kernel ABC and kernel herding to the same observeddata. We provide a theoretical explanation regarding why the approach works,showing (for the population setting) that, under a certain assumption, pointestimates obtained with this method converge to the true parameter, asrecursion proceeds. We have conducted a variety of numerical experiments,including parameter estimation for a real-world pedestrian flow simulator, andshow that in most cases our method outperforms existing approaches.

Kernel Recursive ABC: Point Estimation with Intractable Likelihood