  State-space models (SSMs) provide a flexible framework for modelling
time-series data. Consequently, SSMs are ubiquitously applied in areas such as
engineering, econometrics and epidemiology. In this paper we provide a fast
approach for approximate Bayesian inference in SSMs using the tools of deep
learning and variational inference.
