AISTATS 2016poster19 citations
Enumerating Equivalence Classes of Bayesian Networks using EC Graphs
Eunice Yuh-Jie Chen, Arthur Choi Choi, Adnan Darwiche
Abstract
We consider the problem of learning Bayesian network structures from complete data. In particular, we consider the enumeration of their k-best equivalence classes. We propose a new search space for A* search, called the EC graph, that facilitates the enumeration of equivalence classes, by representing the space of completed, partially directed acyclic graphs. We also propose a canonization of this search space, called the EC tree, which further improves the efficiency of enumeration. Empirically, our approach is orders of magnitude more efficient than the state-of-the-art at enumerating equivalence classes.
BibTeX
@InProceedings{pmlr-v51-chen16b,
title = {Enumerating Equivalence Classes of Bayesian Networks using EC Graphs},
author = {Chen, Eunice Yuh-Jie and Choi, Arthur Choi and Darwiche, Adnan},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {591--599},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/chen16b.pdf},
url = {https://proceedings.mlr.press/v51/chen16b.html},
abstract = {We consider the problem of learning Bayesian network structures from complete data. In particular, we consider the enumeration of their k-best equivalence classes. We propose a new search space for A* search, called the EC graph, that facilitates the enumeration of equivalence classes, by representing the space of completed, partially directed acyclic graphs. We also propose a canonization of this search space, called the EC tree, which further improves the efficiency of enumeration. Empirically, our approach is orders of magnitude more efficient than the state-of-the-art at enumerating equivalence classes.}
}