Nonparametric Bayesian identification of jump systems with sparse dependencies
Author(s)Fox, Emily Beth; Sudderth, Erik B.; Jordan, Michael I.; Willsky, Alan S.
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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 Markov jump linear systems: the switching linear dynamical system (SLDS) and the switching vector autoregressive (S-VAR) process. In this paper, we present a nonparametric Bayesian approach to identifying an unknown number of persistent, smooth dynamical modes by utilizing a hierarchical Dirichlet process prior. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies. The utility and flexibility of our models are demonstrated on synthetic data and a set of honey bee dances.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
15th Symposium on System Identification, SYSID 2009
International Federation of Automatic Control (IFAC)
Emily, Fox. “Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies.” Ed. Walter Eric. 15th Symposium on System Identification, SYSID 2009. 1591–1596. © 2009 IFAC
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