Structure Learning in Graphical Modeling
Citations Over TimeTop 18% of 2016 papers
Abstract
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.
Related Papers
- → Low-Order Conditional Independence Graphs for Inferring Genetic Networks(2006)124 cited
- Mixed graphical models via exponential families(2014)
- → Inferring Networks from High-Dimensional Data with Mixed Variables(2014)1 cited
- → Graph Estimation From Multi-attribute Data(2012)34 cited
- → Tractable Undirected Approximations for Graphical Models(1998)6 cited