Getting started in probabilistic graphical models

被引：30

作者：

Airoldi, Edoardo M. ^{[1
,2
]}

机构：

[1] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08544 USA

[2] Princeton Univ, Dept Comp Sci, Princeton, NJ 08544 USA

来源：

PLOS COMPUTATIONAL BIOLOGY | 2007年 / 3卷 / 12期

关键词：

D O I：

10.1371/journal.pcbi.0030252

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：2421 / 2425

页数：5

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