Bayesian sparse factor models and DAGs inference and comparison

Ricardo Henao, Ole Winther

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

6 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Number of pages9
StatePublished - Dec 1 2009
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15


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