Sequential Variational

Sequential variational inference methods aim to efficiently estimate hidden states in dynamic systems by iteratively updating probabilistic models over time. Current research focuses on developing algorithms like sequential Gaussian variational inference and online variational sequential Monte Carlo, often incorporating techniques from information geometry and stochastic approximation to improve computational efficiency and accuracy, particularly in nonlinear systems. These advancements are impacting diverse fields, including robotics (for state estimation and localization) and signal processing (for tracking non-stationary noise), by enabling more robust and efficient inference in complex, real-world applications. Improved scalability and accuracy compared to traditional methods like particle filters are key advantages.