Unifying DAGs and UGs

Jose M. Peña
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:308-319, 2018.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v72-pena18a, title = {Unifying DAGs and UGs}, author = {Pe\~{n}a, Jose M.}, booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, pages = {308--319}, year = {2018}, editor = {Kratochvíl, Václav and Studený, Milan}, volume = {72}, series = {Proceedings of Machine Learning Research}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/pena18a/pena18a.pdf}, url = {https://proceedings.mlr.press/v72/pena18a.html}, 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.} }
Endnote
%0 Conference Paper %T Unifying DAGs and UGs %A Jose M. Peña %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-pena18a %I PMLR %P 308--319 %U https://proceedings.mlr.press/v72/pena18a.html %V 72 %X 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.
APA
Peña, J.M.. (2018). Unifying DAGs and UGs. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 72:308-319 Available from https://proceedings.mlr.press/v72/pena18a.html.

Related Material