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Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky

Wed Dec 10 11:54 AM -- 11:55 AM (PST) @

Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. In this paper, we present a nonparametric approach to the learning of an unknown number of persistent, smooth dynamical modes by utilizing a hierarchical Dirichlet process prior. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with an efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, and the IBOVESPA stock index.

Author Information

Emily Fox (Stanford University)
Erik Sudderth (University of California, Irvine)
Michael Jordan (UC Berkeley)
Alan S Willsky (Massachusetts Institute of Technology)

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