Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant.

Original publication

DOI

10.1109/TSMCB.2009.2036593

Type

Journal article

Journal

IEEE Trans Syst Man Cybern B Cybern

Publication Date

10/2010

Volume

40

Pages

1231 - 1242

Keywords

Algorithms, Arabidopsis, Bayes Theorem, Computer Simulation, Gene Expression Regulation, Models, Biological, Models, Statistical, Normal Distribution, Plant Proteins, Signal Transduction, Terpenes