Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models (opens in new tab)

Understanding dynamic systems is a central goal in many scientific disciplines. State-space models provide a general framework for studying latent dynamic systems based on indirect observations. However, classical state-space methods require researchers to specify the parametric form of the system dynamics in advance, which can be challenging when the underlying processes are nonlinear and only partially explained by theory. Gaussian process sta...