  Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a
rigorous and viable way to identify changepoints in complex systems. In this
work, we introduce a Stein variational online changepoint detection (SVOCD)
method to provide a computationally tractable generalization of BOCPD beyond
the exponential family of probability distributions. We integrate the recently
developed Stein variational Newton (SVN) method (Detommaso et al., 2018) and
BOCPD to offer a full online Bayesian treatment for a large number of
situations with significant importance in practice. We apply the resulting
method to two challenging and novel applications: Hawkes processes and long
short-term memory (LSTM) neural networks. In both cases, we successfully
demonstrate the efficacy of our method on real data.
