Kernel Recursive ABC: Point Estimation with Intractable Likelihood
This addresses the problem of parameter estimation in complex statistical models for researchers and practitioners, though it appears incremental as it builds on existing kernel ABC and herding techniques.
The paper tackles parameter estimation for simulator-based models with intractable likelihood by recursively applying kernel ABC and kernel herding, showing theoretical convergence to the true parameter and outperforming existing methods in most numerical experiments, including a real-world pedestrian flow simulator.
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.