We present the first variational inference framework for non-Markovian neural SDEs driven by fractional Brownian Motion. Our method builds upon the idea of approximating the fBM by a linear combination of Markov processes, driven by the same, Brownian motion. We then provide the variational prior and posterior, as well as the EBLO.
