D-Separation: From Theorems to Algorithms
Abstract
An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.
Cite
Text
Geiger et al. "D-Separation: From Theorems to Algorithms." Conference on Uncertainty in Artificial Intelligence, 1989.Markdown
[Geiger et al. "D-Separation: From Theorems to Algorithms." Conference on Uncertainty in Artificial Intelligence, 1989.](https://mlanthology.org/uai/1989/geiger1989uai-dseparation/)BibTeX
@inproceedings{geiger1989uai-dseparation,
title = {{D-Separation: From Theorems to Algorithms}},
author = {Geiger, Dan and Verma, Tom S. and Pearl, Judea},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1989},
url = {https://mlanthology.org/uai/1989/geiger1989uai-dseparation/}
}