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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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  Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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