Learning structural changes of Gaussian graphical models in controlled experiments

Bai Zhang, Yue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graphical models are widely used in scientific and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce significant structural changes in the corresponding graphical models. Therefore, it is of great importance to be able to detect such structural changes from data, so as to gain novel insights into where and how the structural changes take place and help the system adapt to the new environment. Here we report an effective learning strategy to extract structural changes in Gaussian graphical model using '1-regularization based convex optimization. We discuss the properties of the problem formulation and introduce an efficient implementation by the block coordinate descent algorithm. We demonstrate the principle of the approach on a numerical simulation experiment, and we then apply the algorithm to the modeling of gene regulatory networks under different conditions and obtain promising yet biologically plausible results.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010
PublisherAUAI Press
Pages701-708
Number of pages8
ISBN (Print)9780974903965
StatePublished - 2010

Publication series

NameProceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Learning structural changes of Gaussian graphical models in controlled experiments'. Together they form a unique fingerprint.

Cite this