Sequential Estimation of Gaussian Process-based Deep State-Space Models

arXiv:2301.12528v2 Announce Type: replace
Abstract: We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models. The proposed approach relies on Gaussian and deep Gaussian processes that are implemented via random feature-based Gaussian processes. In these models, we have two sets of unknowns, highly nonlinear unknowns (the values of the latent processes) and conditionally linear unknowns (the constant parameters of the random feature-based Gaussian …

abstract arxiv cs.lg feature functions gaussian processes process processes random space state type via