ICML 2018oral20 citations
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
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.
BibTeX
@InProceedings{pmlr-v80-kajihara18a,
title = {Kernel Recursive {ABC}: Point Estimation with Intractable Likelihood},
author = {Kajihara, Takafumi and Kanagawa, Motonobu and Yamazaki, Keisuke and Fukumizu, Kenji},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2400--2409},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/kajihara18a/kajihara18a.pdf},
url = {https://proceedings.mlr.press/v80/kajihara18a.html},
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.}
}