• Computer Science
  • Published in PGM 2018

Structure Learning for Bayesian Networks over Labeled DAGs

@inproceedings{Hyttinen2018StructureLF,
  title={Structure Learning for Bayesian Networks over Labeled DAGs},
  author={Antti Hyttinen and Johan Pensar and Juha Kontinen and Jukka Corander},
  booktitle={PGM},
  year={2018}
}
Graphical models based on labeled directed acyclic graphs (LDAGs) allow for representing contextspecific independence relations in addition to regular conditional independencies. Modeling such constraints has been demonstrated to be important for expressiveness, interpretation and predictive ability. In this paper, we build theoretical results that make constraint-based and exact score-based structure discovery possible for this interesting model class. In detail, we present the first… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 27 REFERENCES

Learning Bayesian Networks with Local Structure

  • Learning in Graphical Models
  • 1996
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL