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arXiv 2012.050722020-12stat.MLcs.LGcs.SYeess.SYstat.ME

Variational System Identification for Nonlinear State-Space Models

University of Newcastle（纽卡斯尔大学）；Uppsala University（乌普萨拉大学）

Jarrad Courts, Adrian Wills, Thomas Schön, Brett Ninness

英文摘要：

This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, which is a principled method that has deep connections to maximum likelihood estimation. This VI approach ultimately provides estimates of the model as solutions to an optimisation problem, which is deterministic, tractable and can be solved using standard optimisation tools. A specialisation of this approach for systems with additive Gaussian noise is also detailed. The proposed method is examined numerically on a range of simulated and real examples focusing on the robustness to parameter initialisation; additionally, favourable comparisons are performed against state-of-the-art alternatives.

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