|Stochastic Systems Group|
Switching Autoregressive Trees
We consider the problem of inference over evolving dependency structures of multiple vector time-series. Specifically, we develop a fully Bayesian approach for inference over directed tree models as a means of examining the influence of time-series on each other. This analysis is embedded in a dynamic setting in which a latent variable indexes evolving tree structures. We demonstrate the utility of the method by analyzing the interaction of moving objects.
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