  Approximate variational inference has shown to be a powerful tool for
modeling unknown complex probability distributions. Recent advances in the
field allow us to learn probabilistic models of sequences that actively exploit
spatial and temporal structure. We apply a Stochastic Recurrent Network (STORN)
to learn robot time series data. Our evaluation demonstrates that we can
robustly detect anomalies both off- and on-line.
