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Sharing Features among Dynamical Systems with Beta Processes
Emily Fox · Erik Sudderth · Michael Jordan · Alan S Willsky

Wed Dec 09 04:30 PM -- 04:50 PM (PST) @

We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors. Using a beta process prior, we allow data-driven selection of the size of this set, as well as the pattern with which behaviors are shared among time series. Via the Indian buffet process representation of the beta process' predictive distributions, we develop an exact Markov chain Monte Carlo inference method. In particular, our approach uses the sum-product algorithm to efficiently compute Metropolis-Hastings acceptance probabilities, and explores new dynamical behaviors via birth/death proposals. We validate our sampling algorithm using several synthetic datasets, and also demonstrate promising unsupervised segmentation of visual motion capture data.

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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