Bibliographic Details
| Title: |
Kernel Recursive ABC: Point Estimation with Intractable Likelihood |
| Authors: |
Kajihara, Takafumi, Kanagawa, Motonobu, Yamazaki, Keisuke, Fukumizu, Kenji |
| Publication Year: |
2018 |
| Collection: |
Statistics |
| Subject Terms: |
Statistics - Machine Learning |
| More Details: |
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.
Comment: to appear in ICML 2018. 18 pages |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/1802.08404 |
| Accession Number: |
edsarx.1802.08404 |
| Database: |
arXiv |
