Kernel Recursive ABC: Point Estimation with Intractable Likelihood
2018
Conference Paper
pn
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
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BibTex @conference{KajKanYamFuk18, 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}, doi = {}, month_numeric = {7} } |