dc.contributor.author |
Bouckaert, Remco |
en |
dc.contributor.author |
Hemmecke, R |
en |
dc.contributor.author |
Lindner, S |
en |
dc.contributor.author |
Studeny, M |
en |
dc.date.accessioned |
2012-03-01T20:57:56Z |
en |
dc.date.issued |
2010-12 |
en |
dc.identifier.citation |
Journal of Machine Learning Research 11:3453-3479 Dec 2010 |
en |
dc.identifier.issn |
1532-4435 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/12519 |
en |
dc.description.abstract |
The topic of the paper is computer testing of (probabilistic) conditional independence (CI) implications by an algebraic method of structural imsets. The basic idea is to transform (sets of) CI statements into certain integral vectors and to verify by a computer the corresponding algebraic relation between the vectors, called the independence implication. We interpret the previous methods for computer testing of this implication from the point of view of polyhedral geometry. However, the main contribution of the paper is a new method, based on linear programming (LP). The new method overcomes the limitation of former methods to the number of involved variables. We recall/ describe the theoretical basis for all four methods involved in our computational experiments, whose aim was to compare the efficiency of the algorithms. The experiments show that the LP method is clearly the fastest one. As an example of possible application of such algorithms we show that testing inclusion of Bayesian network structures or whether a CI statement is encoded in an acyclic directed graph can be done by the algebraic method. |
en |
dc.language |
EN |
en |
dc.publisher |
MICROTOME PUBL |
en |
dc.relation.ispartofseries |
Journal of Machine Learning Research |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
conditional independence inference |
en |
dc.subject |
linear programming approach |
en |
dc.title |
Efficient Algorithms for Conditional Independence Inference |
en |
dc.type |
Journal Article |
en |
pubs.begin-page |
3453 |
en |
pubs.volume |
11 |
en |
dc.rights.holder |
Copyright: Microtome Publishing ; The Authors |
en |
pubs.author-url |
http://web.ebscohost.com.ezproxy.auckland.ac.nz/ehost/pdfviewer/pdfviewer?sid=173cca16-1d5d-46e2-905e-d10f237121a7%40sessionmgr15&vid=2&hid=19 |
en |
pubs.end-page |
3479 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
261148 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
pubs.record-created-at-source-date |
2012-02-22 |
en |