AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
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Publication:6314462
arXiv1902.08480MaRDI QIDQ6314462
Author name not available (Why is that?)
Publication date: 22 February 2019
Abstract: Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the underlying stochastic system. Using state-of-the-art adversarial and moment matching inference techniques, we avoid the discretization schemes of classical approaches. This leads to significant improvements in parameter accuracy and robustness given random initial guesses. On four established benchmark systems, we compare the performance of our algorithms to state-of-the-art solutions based on extended Kalman filtering and Gaussian processes.
Has companion code repository: https://github.com/gabb7/AReS-MaRS
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