Learning graphical models for stationary time series

@article{Bach2004LearningGM,
  title={Learning graphical models for stationary time series},
  author={Francis R. Bach and Michael I. Jordan},
  journal={IEEE Transactions on Signal Processing},
  year={2004},
  volume={52},
  pages={2189-2199}
}
Probabilistic graphical models can be extended to time series by considering probabilistic dependencies between entire time series. For stationary Gaussian time series, the graphical model semantics can be expressed naturally in the frequency domain, leading to interesting families of structured time series models that are complementary to families defined in the time domain. In this paper, we present an algorithm to learn the structure from data for directed graphical models for stationary… CONTINUE READING
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