Gaussian Process State Space Model

Gaussian Process State-Space Models (GPSSMs) are probabilistic models used to represent complex, nonlinear dynamical systems by placing a Gaussian Process prior on the system's transition function. Current research focuses on improving computational efficiency, particularly for high-dimensional systems, through techniques like variational inference, ensemble Kalman filtering, and the incorporation of efficient transformed Gaussian processes or multi-resolution architectures. These advancements enable applications in diverse fields, including robotics (trajectory estimation, control), environmental monitoring (data assimilation, coverage control), and energy forecasting (solar power prediction), where accurate modeling of uncertainty and efficient online inference are crucial. The ability to incorporate prior knowledge and handle both continuous and discrete-time data makes GPSSMs a powerful tool for a wide range of applications.