  We consider the Bayesian optimal filtering problem: i.e. estimating some
conditional statistics of a latent time-series signal from an observation
sequence. Classical approaches often rely on the use of assumed or estimated
transition and observation models. Instead, we formulate a generic recurrent
neural network framework and seek to learn directly a recursive mapping from
observational inputs to the desired estimator statistics. The main focus of
this article is the approximation capabilities of this framework. We provide
approximation error bounds for filtering in general non-compact domains. We
also consider strong time-uniform approximation error bounds that guarantee
good long-time performance. We discuss and illustrate a number of practical
concerns and implications of these results.

Machine Learning;Machine Learning;Signal Processing;Optimization and Control