Bayesian Nonparametric Inference of Switching Dynamic Linear Models
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HDPSLDS_journal_v5.pdf
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Author(s)
Fox, Emily Beth
Sudderth, Erik B.
Jordan, Michael I.
Willsky, Alan S.
Date Issued
January 2011
Journal
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Fox, Emily, Erik B. Sudderth, Michael I. Jordan, and Alan S. Willsky. Bayesian Nonparametric Inference of Switching Dynamic Linear Models. IEEE Transactions on Signal Processing 59, no. 4 (April 2011): 1569-1585.
Version
Author's final manuscript
Abstract
Many complex 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. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with 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, the IBOVESPA stock index and a maneuvering target tracking application.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/tsp.2010.2102756
