# Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks Source: [Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:35:37.665Z Authors: Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir Published: 2019-01-23T16:35:00.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Change Point Detection on Synthetic Hawkes Process dataset](): Change Point Detection; Synthetic Hawkes Process dataset; FNR: 33. - [Change Point Detection on WannaCry Cyber Attack](): Change Point Detection; WannaCry Cyber Attack; FNR: 34. - [Change Point Detection on NYC Vehicle Collisions](): Change Point Detection; NYC Vehicle Collisions; FNR: 22. ## Original sources and implementation [Original paper]() [PDF]()