Gaussian Processes (GPs) have become an increasingly popular framework for nonlinear regression and learning in a variety of online applications such as control, reinforcement learning, and machine learning. Most GP algorithms assume a static world, and will perform poorly if the generating process switches at discrete instants in time (changepoints). However, many applications of interest, such as controlling an aircraft or predicting the stock market, may include such changepoints.
