Interaction analysis using switching structured autoregressive models

This paper explores modeling the dependency structure among multiple vector time-series. We focus on a large classes of structures which yield efficient and tractable exact inference. Specifically, we use directed trees and forests to model causal interactions among time-series. These models are incorporated in a dynamic setting in which a latent variable indexes evolving structures. We demonstrate the utility of the method by analyzing the interaction of multiple moving objects.