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Exploiting within-Clique factorizations in junction-tree algorithms

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dc.contributor.authorMcAuley, Julian
dc.contributor.authorCaetano, Tiberio
dc.coverage.spatialSardinia Italy
dc.date.accessioned2015-12-10T22:43:45Z
dc.date.createdMay 13-15 2010
dc.date.issued2010
dc.date.updated2016-02-24T11:45:03Z
dc.description.abstractWe show that the expected computational complexity of the Junction-Tree Algorithm for maximum a posteriori inference in graphical models can be improved. Our results apply whenever the potentials over maximal cliques of the triangulated graph are factored over subcliques. This is common in many real applications, as we illustrate with several examples. The new algorithms are easily implemented, and experiments show substantial speed-ups over the classical Junction-Tree Algorithm. This enlarges the class of models for which exact inference is efficient.
dc.identifier.urihttp://hdl.handle.net/1885/58301
dc.publisherSociety for Artificial Intelligence and Statistics
dc.relation.ispartofseriesInternational Conference on Artificial Intelligence and Statistics (AISTATS 2010)
dc.sourceProceedings of The 13th International Conference on Artificial Intelligence and Statistics(AISTATS-2010)
dc.source.urihttp://jmlr.csail.mit.edu/proceedings/papers/v9
dc.subjectKeywords: Exact inference; GraphicaL model; Maximal clique; Maximum a posteriori; Real applications; Algorithms; Artificial intelligence; Forestry; Algorithms; Artificial Intelligence; Forestry
dc.titleExploiting within-Clique factorizations in junction-tree algorithms
dc.typeConference paper
local.bibliographicCitation.lastpage532
local.bibliographicCitation.startpage525
local.contributor.affiliationMcAuley, Julian, College of Engineering and Computer Science, ANU
local.contributor.affiliationCaetano, Tiberio, College of Engineering and Computer Science, ANU
local.contributor.authoruidMcAuley, Julian, u4291439
local.contributor.authoruidCaetano, Tiberio, u4590840
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB436
local.identifier.scopusID2-s2.0-84860172533
local.type.statusPublished Version

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