Getting Started in Probabilistic Graphical Models

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Getting Started in Probabilistic Graphical Models

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dc.contributor.author Airoldi, Edoardo
dc.date.accessioned 2009-03-27T19:53:16Z
dc.date.issued 2007
dc.identifier.citation Airoldi, Edoardo M. 2007. Getting Started in Probabilistic Graphical Models. PLoS Computational Biology 3(12): e252. doi:10.1371/journal.pcbi.0030252 en
dc.identifier.issn 1553-734X en
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:2757496
dc.description.abstract Probabilistic 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.sponsorship Statistics en
dc.language.iso en_US en
dc.publisher Public Library of Science en
dc.relation.isversionof http://dx.doi.org/10.1371/journal.pcbi.0030252 en
dash.license LAA
dc.title Getting Started in Probabilistic Graphical Models en
dc.type Journal Article
dc.description.version Version of Record
dc.relation.journal PLoS Computational Biology en
dash.depositing.author Airoldi, Edoardo

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  • FAS Scholarly Articles [7594]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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