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2011

Bayesian Nonparametric Inference of Switching Dynamic Linear Models

8 years 3 months ago
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
—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.
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where TSP
Authors Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky
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