Bayesian Sparse Factor Models and DAGs Inference and Comparison
Ricardo Henao, Ole Winther · 2009
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 com-parison between them. For this purpose, we exploit the connection between factor models and DAGs to propose Bayesian hierarchies based on spike and slab pri-ors 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 struc-tures. The effectiveness of our approach is demonstrated through extensive exper-iments on artificial and biological data showing that our approach outperform a number of state of the art methods. 1