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

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
On Expressiveness of the AMP Chain Graph Interpretation
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology. (ADIT)
2014 (English)In: Probabilistic Graphical Models: 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings, Springer, 2014, 458-470 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we study the expressiveness of the Andersson-Madigan-Perlman interpretation of chain graphs. It is well known that all independence models that can be represented by Bayesian networks also can be perfectly represented by chain graphs of the Andersson-Madigan-Perlman interpretation but it has so far not been studied how much more expressive this second class of models is. In this paper we calculate the exact number of representable independence models for the two classes, and the ratio between them, for up to five nodes. For more than five nodes the explosive growth of chain graph models does however make such enumeration infeasible. Hence we instead present, and prove the correctness of, a Markov chain Monte Carlo approach for sampling chain graph models uniformly for the Andersson-Madigan-Perlman interpretation. This allows us to approximate the ratio between the numbers of independence models representable by the two classes as well as the average number of chain graphs per chain graph model for up to 20 nodes. The results show that the ratio between the numbers of representable independence models for the two classes grows exponentially as the number of nodes increases. This indicates that only a very small fraction of all independence models representable by chain graphs of the Andersson-Madigan-Perlman interpretation also can be represented by Bayesian networks.

Place, publisher, year, edition, pages
Springer, 2014. 458-470 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 8754
Keyword [en]
Chain graphs, Andersson-Madigan-Perlman interpretation, MCMC sampling
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-111786DOI: 10.1007/978-3-319-11433-0_30ISI: 000358253800030Scopus ID: 2-s2.0-84921764539ISBN: 978-3-319-11432-3 (print)ISBN: 978-3-319-11433-0 (print)OAI: oai:DiVA.org:liu-111786DiVA: diva2:760241
Conference
7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014
Funder
Swedish Research Council, 2010-4808
Available from: 2014-11-03 Created: 2014-11-03 Last updated: 2015-08-20

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Sonntag, Dag

Search in DiVA

By author/editor
Sonntag, Dag
By organisation
Database and information techniquesThe Institute of Technology
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 42 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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