Description
Many problems in science and engineering involve the modeling of dynamic processes using state-space models (SSMs). Online parameter and state estimation---computing the posterior probability for both static parameters and dynamic states, incrementally over time–--is crucial for many applications. Many sequential Monte Carlo algorithms have been proposed for this problem; some apply only to restricted model classes, while others are computationally expensive. We propose two new algorithms, namely, the extended parameter filter and the assumed parameter filter, that try to close the gap between computational efficiency and generality. We compare our new algorithms with several state-of-the-art solutions on many benchmark problems. Finally, we discuss our work on joint state and parameter estimation for physiological models in intensive-care medicine.