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.
Takafumi Kajihara (NEC)
Motonobu Kanagawa (Max Planck Institute for Intelligent Systems)
Keisuke Yamazaki (National Institute of Advanced Industrial Science and Technology)
Kenji Fukumizu (Institute of Statistical Mathematics)
Related Events (a corresponding poster, oral, or spotlight)
2018 Oral: Kernel Recursive ABC: Point Estimation with Intractable Likelihood »
Thu Jul 12th 12:50 -- 01:10 PM Room A3