Bayesian sparse factor models and DAGs inference and comparison

Conference Paper

In this paper we present a novel approach to learn directed acyclic graphs (DAGs) and factor models within the same framework while also allowing for model comparison between them. For this purpose, we exploit the connection between factor models and DAGs to propose Bayesian hierarchies based on spike and slab priors to promote sparsity, heavy-tailed priors to ensure identifiability and predictive densities to perform the model comparison. We require identifiability to be able to produce variable orderings leading to valid DAGs and sparsity to learn the structures. The effectiveness of our approach is demonstrated through extensive experiments on artificial and biological data showing that our approach outperform a number of state of the art methods.

Duke Authors

Cited Authors

  • Henao, R; Winther, O

Published Date

  • December 1, 2009

Published In

  • Advances in Neural Information Processing Systems 22 Proceedings of the 2009 Conference

Start / End Page

  • 736 - 744

International Standard Book Number 13 (ISBN-13)

  • 9781615679119

Citation Source

  • Scopus