Unifying DAGs and UGs
Machine Learning
2018-03-02 v8 Artificial Intelligence
Abstract
We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden. Moreover, up to two edges are allowed between any pair of nodes. Specifically, we present local, pairwise and global Markov properties for the new graphical models and prove their equivalence. We also present an equivalent factorization property. Finally, we present a causal interpretation of the new models.
Keywords
Cite
@article{arxiv.1708.08722,
title = {Unifying DAGs and UGs},
author = {Jose M. Peña},
journal= {arXiv preprint arXiv:1708.08722},
year = {2018}
}
Comments
v2: A factorization property has been added in Section 5. v3: Minor errors corrected. v4: Additional example added. v5: Learning algorithm added. v6: Section 6 added. v7: Appendix B added