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
Error AMP Chain Graphs
Linköping University, Department of Computer and Information Science, Database and information techniques. (ADIT)
2013 (English)In: TWELFTH SCANDINAVIAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (SCAI 2013), IOS Press, 2013, 215-224 p.Conference paper, Published paper (Refereed)
Abstract [en]

Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose structure depends on the CG. However, the CG represents the errors implicitly, as no nodes in the CG correspond to the errors. We propose in this paper to add some deterministic nodes to the CG in order to represent the errors explicitly. We call the result an EAMP CG. We will show that, as desired, every AMP CG is Markov equivalent to its corresponding EAMP CG under marginalization of the error nodes. We will also show that every EAMP CG under marginalization of the error nodes is Markov equivalent to some LWF CG under marginalization of the error nodes, and that the latter is Markov equivalent to some directed and acyclic graph (DAG) under marginalization of the error nodes and conditioning on some selection nodes. This is important because it implies that the independence model represented by an AMP CG can be accounted for by some data generating process that is partially observed and has selection bias. Finally, we will show that EAMP CGs are closed under marginalization. This is a desirable feature because it guarantees parsimonious models under marginalization.

Place, publisher, year, edition, pages
IOS Press, 2013. 215-224 p.
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 257
Keyword [en]
probabilistic graphical models; directed and acyclic graphs; chain graphs
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-98071DOI: 10.3233/978-1-61499-330-8-215ISI: 000343477100023ISBN: 978-1-61499-329-2 (print)OAI: oai:DiVA.org:liu-98071DiVA: diva2:651938
Conference
12th Scandinavian Conference on Artificial Intelligence (SCAI 2013)
Available from: 2013-09-27 Created: 2013-09-27 Last updated: 2014-11-28

Open Access in DiVA

fulltext(270 kB)54 downloads
File information
File name FULLTEXT01.pdfFile size 270 kBChecksum SHA-512
af65f973907ef53c61d004561fc3f78798188c7e619b35f0280e64ee6d256b8ec51195462c808b737d51689089ef6ead5316b065a88893cbe163e67577991ffb
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Peña, Jose M.

Search in DiVA

By author/editor
Peña, Jose M.
By organisation
Database and information techniques
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 54 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 123 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