3 accepted papers
We design a variational state estimation (VSE) method that provides a closed-form Gaussian posterior of an underlying complex dynamical process from (noisy) nonlinear measurements. The complex process is model-free. That is, we do not have a suitable physics-based model characterizing the temporal e…
We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The ‘model-free process’ means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) a…
We introduce a model-free hidden sequence (MHS) estimation problem where the task is to estimate a long sequence of ‘model-free’ process hidden under additive Gaussian noise. To estimate the posterior of the hidden sequence from the noisy observation sequence, we have three main challenges: (a) the…