%0 Journal Article %1 10.1371/journal.pcbi.0030252 %A Airoldi, Edoardo M %D 2007 %I Public Library of Science %J PLoS Comput Biol %K probabilistic_graphical_model probability tutorial %N 12 %P e252 %R 10.1371/journal.pcbi.0030252 %T Getting Started in Probabilistic Graphical Models %V 3 %X 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.

@article{10.1371/journal.pcbi.0030252, 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.}, added-at = {2009-10-14T04:05:43.000+0200}, author = {Airoldi, Edoardo M}, biburl = {https://www.bibsonomy.org/bibtex/28ebb1a01e7d5f6da97f4c08c7b1392c8/ytyoun}, doi = {10.1371/journal.pcbi.0030252}, interhash = {e8427cd0095af26bc06ebc8b4ec4fef2}, intrahash = {8ebb1a01e7d5f6da97f4c08c7b1392c8}, journal = {PLoS Comput Biol}, keywords = {probabilistic_graphical_model probability tutorial}, month = {12}, number = 12, pages = {e252}, publisher = {Public Library of Science}, timestamp = {2016-06-14T14:36:56.000+0200}, title = {Getting Started in Probabilistic Graphical Models}, volume = 3, year = 2007 }