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dc.contributor.authorAiroldi, Edoardo
dc.date.accessioned2009-03-27T19:53:16Z
dc.date.issued2007
dc.identifier.citationAiroldi, Edoardo M. 2007. Getting Started in Probabilistic Graphical Models. PLoS Computational Biology 3(12): e252. doi:10.1371/journal.pcbi.0030252en
dc.identifier.issn1553-734Xen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:2757496
dc.description.abstractProbabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses that are testable at the bench? This Message sketches out some answers and illustrates the main ideas behind the statistical approach to biological pattern discovery.en
dc.description.sponsorshipStatisticsen
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.0030252en
dash.licenseLAA
dc.titleGetting Started in Probabilistic Graphical Modelsen
dc.typeJournal Article
dc.description.versionVersion of Record
dc.relation.journalPLoS Computational Biologyen
dash.depositing.authorAiroldi, Edoardo
dc.identifier.doi10.1371/journal.pcbi.0030252*
dash.contributor.affiliatedAiroldi, Edoardo


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