Reading Dependencies from Polytree-Like Bayesian Networks
Artificial Intelligence
2012-06-26 v1 Machine Learning
Machine Learning
Abstract
We present a graphical criterion for reading dependencies from the minimal directed independence map G of a graphoid p when G is a polytree and p satisfies composition and weak transitivity. We prove that the criterion is sound and complete. We argue that assuming composition and weak transitivity is not too restrictive.
Keywords
Cite
@article{arxiv.1206.5263,
title = {Reading Dependencies from Polytree-Like Bayesian Networks},
author = {Jose M. Pena},
journal= {arXiv preprint arXiv:1206.5263},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)