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Kernel Recursive ABC: Point Estimation with Intractable Likelihood
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.
@conference{KajKanYamFuk18, title = {Kernel Recursive {ABC}: Point Estimation with Intractable Likelihood}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, 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.}, pages = {2405--2414}, publisher = {PMLR}, month = jul, year = {2018}, slug = {kajkanyamfuk18}, author = {Kajihara, T. and Kanagawa, M. and Yamazaki, K. and Fukumizu, K.}, month_numeric = {7} }