Learning minimal latent directed information trees

@article{Etesami2012LearningML,
  title={Learning minimal latent directed information trees},
  author={Jalal Etesami and Negar Kiyavash and Todd P. Coleman},
  journal={2012 IEEE International Symposium on Information Theory Proceedings},
  year={2012},
  pages={2726-2730}
}
THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD - We propose a framework for learning the structure of a minimal latent tree with an associated discrepancy measure. Specifically, we apply this algorithm to recover the minimal latent directed information tree on a mixture of set of observed and unobserved random processes. Directed information trees are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a… CONTINUE READING

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SHOWING 1-10 OF 13 REFERENCES

Equivalence between minimal generative model graphs and directed information graphs

  • 2011 IEEE International Symposium on Information Theory Proceedings
  • 2011
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Universal entropy estimation via block sorting

  • IEEE Transactions on Information Theory
  • 2004
VIEW 1 EXCERPT

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