Abstract 

We present the first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with β-divergences. The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a construction for exponential models and demonstrate it on the Bayesian Linear Regression model. In so doing, we make two additional contributions: Firstly, we make GBI scalable using Structural Variational approximations that are exact as β 0. Secondly, we give a principled way of choosing the divergence parameter β by minimizing expected predictive loss on-line. Reducing False Discovery Rates of CP S from over 90% to 0% on real world data, this offers the state of the art.