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Getting started in probabilistic graphical models

Airoldi, EM
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Review
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Date
2007-12-01
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Subject
Algorithms
 Computer Graphics
 Computer Simulation
 Data Interpretation, Statistical
 Models, Biological
 Models, Statistical
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http://hdl.handle.net/20.500.12613/5613
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Getting started in probabilistic graphical models.pdf
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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. © 2007 Edoardo M. Airoldi.
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PLoS Computational Biology
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