Getting Started in Probabilistic Graphical Models
| Title: | Getting Started in Probabilistic Graphical Models |
| Author: | Airoldi, Edoardo |
| Citation: | Airoldi, Edoardo M. 2007. Getting Started in Probabilistic Graphical Models. PLoS Computational Biology 3(12): e252. doi:10.1371/journal.pcbi.0030252 |
| Full Text & Related Files: |
Airoldi_GettingStarted.pdf (324.3Kb; PDF) 
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| 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. |
| Published Version: | http://dx.doi.org/10.1371/journal.pcbi.0030252 |
| Terms of Use: | This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:2757496 |
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Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
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