Variational system identification for nonlinear state-space models

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, principled method that has deep connections to maximum likelihood estimation. VI approach ultimately provides estimates of the model as solutions optimisation problem, deterministic, tractable and can be solved using standard tools. A specialisation systems with additive Gaussian noise also detailed. The proposed examined numerically on range simulated real examples focusing robustness initialisation; additionally, favourable comparisons are performed against state-of-the-art alternatives.