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Kernel Recursive ABC: Point Estimation with Intractable Likelihood


Conference Paper


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.

Author(s): T. Kajihara and M. Kanagawa and K. Yamazaki and K. Fukumizu
Book Title: Proceedings of the 35th International Conference on Machine Learning
Pages: 2405--2414
Year: 2018
Month: July
Day: 10--15
Publisher: PMLR

Department(s): Probabilistic Numerics
Bibtex Type: Conference Paper (conference)
Paper Type: Conference

Links: Paper


  title = {Kernel Recursive {ABC}: Point Estimation with Intractable Likelihood},
  author = {Kajihara, T. and Kanagawa, M. and Yamazaki, K. and Fukumizu, K.},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning},
  pages = {2405--2414},
  publisher = {PMLR},
  month = jul,
  year = {2018},
  month_numeric = {7}