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- W1966382076 abstract "Abstract 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." @default.
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- W1966382076 date "2009-01-01" @default.
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- W1966382076 title "Nonparametric Bayesian Identification of Jump Systems with Sparse Dependencies" @default.
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- W1966382076 doi "https://doi.org/10.3182/20090706-3-fr-2004.00264" @default.
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