  We present the very first robust Bayesian Online Changepoint Detection
algorithm through General Bayesian Inference (GBI) with $\beta$-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 $\beta \to 0$. Secondly, we give a principled
way of choosing the divergence parameter $\beta$ by minimizing expected
predictive loss on-line. Reducing False Discovery Rates of CPs from more than
90% to 0% on real world data, this offers the state of the art.
