Learning graphical models for stationary time series

  title={Learning graphical models for stationary time series},
  author={Francis R. Bach and Michael I. Jordan},
  journal={IEEE Transactions on Signal Processing},
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
Highly Influential
This paper has highly influenced 10 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 87 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.
55 Citations
28 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 55 extracted citations

87 Citations

Citations per Year
Semantic Scholar estimates that this publication has 87 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 28 references

Graphical Models

  • S. L. Lauritzen
  • London, U.K.: Clarendon
  • 1996
Highly Influential
5 Excerpts

Time Series: Theory and Methods

  • P. J. Brockwell, R. A. Davis
  • New York: Springer-Verlag
  • 1991
Highly Influential
8 Excerpts

and V

  • J. Weston, O. Chapelle, A. Elisseeff, B. Schölkopf
  • Vapnik, “Kernel dependency estimation,” in Adv…
  • 2003
1 Excerpt

Similar Papers

Loading similar papers…