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arXiv · 1802.08404

Kernel Recursive ABC: Point Estimation with Intractable Likelihood

Takafumi Kajihara·Motonobu Kanagawa·Keisuke Yamazaki·Kenji Fukumizu
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Abstract

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.

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Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu. 2018-02-23. Kernel Recursive ABC: Point Estimation with Intractable Likelihood. https://arxiv.org/abs/1802.08404

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