liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Unifying DAGs and UGs
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. (STIMA)
2018 (English)In: Proceedings of the 9th International Conference on Probabilistic Graphical Models (PGM 2018) - Proceedings of Machine Learning Research 72, ML Research Press , 2018, Vol. 72, p. 308-319Conference paper, Published paper (Refereed)
Abstract [en]

We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenbergchain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden. Moreover, up to two edges are allowed between any pair of nodes. Specifically, we present local, pairwise and global Markov properties for the new graphical models and prove their equivalence. We also present an equivalent factorization property.

Place, publisher, year, edition, pages
ML Research Press , 2018. Vol. 72, p. 308-319
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 72
Keywords [en]
Directed acyclic graphs, undirected graphs, chain graphs, Markov properties.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-159302Scopus ID: 2-s2.0-85075487867OAI: oai:DiVA.org:liu-159302DiVA, id: diva2:1340784
Conference
the 9th International Conference on Probabilistic Graphical Models (PGM 2018), Prague, Czech Republic, September 11 - 14, 2018
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2024-09-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

ScopusPaper in proceedings

Authority records

Peña, Jose M.

Search in DiVA

By author/editor
Peña, Jose M.
By organisation
The Division of Statistics and Machine LearningFaculty of Science & Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 171 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf