Directed acyclic graphs (DAGs) have been widely used as a representation of conditional independence in machine learning and statistics. Moreover, hidden or latent variables are often an important component of graphical models. However, DAG models suffer from an important limitation: the family of DAGs is not closed under marginalization of hidden variables… (More)

@article{Silva2009TheHL,
title={The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models},
author={Ricardo Bezerra de Andrade e Silva and Zoubin Ghahramani},
journal={Journal of Machine Learning Research},
year={2009},
volume={10},
pages={1187-1238}
}