Probabilistic State

Probabilistic state estimation focuses on determining the likely state of a system given noisy or incomplete observations, aiming to quantify uncertainty in the estimate. Current research emphasizes efficient algorithms for handling nonlinearities and non-Gaussian distributions, including variations of Kalman filters, particle filters, and variational inference methods, often integrated with neural networks for improved scalability and accuracy. These advancements are crucial for robust control in robotics, safe operation of complex systems like district heating grids, and improved Bayesian inference in diverse scientific applications requiring accurate state estimation under uncertainty.