Structure Learning in Graphical Modeling

被引:128
作者
Drton, Mathias [1 ]
Maathuis, Marloes H. [2 ]
机构
[1] Univ Washington, Dept Stat, Seattle, WA 98195 USA
[2] Swiss Fed Inst Technol, Seminar Stat, CH-8092 Zurich, Switzerland
来源
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 4 | 2017年 / 4卷
关键词
Bayesian network; graphical model; Markov random field; model selection; multivariate statistics; network reconstruction; MARKOV EQUIVALENCE CLASSES; INVERSE COVARIANCE ESTIMATION; BAYESIAN NETWORK STRUCTURE; DIRECTED ACYCLIC GRAPHS; CAUSAL DISCOVERY; PROBABILITY-DISTRIBUTIONS; LIKELIHOOD-ESTIMATION; MAXIMUM-LIKELIHOOD; VARIABLE SELECTION; EFFICIENT METHODS;
D O I
10.1146/annurev-statistics-060116-053803
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
A graphical model is a statistical model that is associated with a graph whose nodes correspond to variables of interest. The edges of the graph reflect allowed conditional dependencies among the variables. Graphical models have computationally convenient factorization properties and have long been a valuable tool for tractable modeling of multivariate distributions. More recently, applications such as reconstructing gene regulatory networks from gene expression data have driven major advances in structure learning, that is, estimating the graph underlying a model. We review some of these advances and discuss methods such as the graphical lasso and neighborhood selection for undirected graphical models (or Markov random fields) and the PC algorithm and score-based search methods for directed graphical models (or Bayesian networks). We further review extensions that account for effects of latent variables and heterogeneous data sources.
引用
收藏
页码:365 / 393
页数:29
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