Speaker: Simon Wilson

Abstract: The dynamic Bayesian regression tree is a flexible regression model for sequential data that permits the relationship between the response and explanatory variables to evolve smoothly over time through a latent process. As such it is suited to tasks involving concept drift and active learning. This paper shows that exact sequential inference can be performed via implementation of the intermittent Kalman filter, permitting fast computation. Inference on the tree structure is done through an ensemble approach and an exact expression for the posterior weight of each tree in the ensemble can be derived. Extensions of this work will be discussed.