System identification using Bayesian neural networks with nonparametric noise models. (arXiv:2104.12119v3 [stat.ME] UPDATED)

System identification is of special interest in science and engineering. This
article is concerned with a system identification problem arising in stochastic
dynamic systems, where the aim is to estimate the parameters of a system along
with its unknown noise processes. In particular, we propose a Bayesian
nonparametric approach for system identification in discrete time nonlinear
random dynamical systems assuming only the order of the Markov process is
known. The proposed method replaces the assumption of Gaussian distributed
error components …

arxiv bayesian identification networks neural networks noise