On Bayesian Network Approximation by Edge Deletion
Abstract
We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for deleting network edges, which is based on the evidence at hand. We provide some interesting bounds on the KL-divergence between original and approximate networks, which highlight the impact of given evidence on the quality of approximation and shed some light on good and bad candidates for edge deletion. We finally demonstrate empirically the promise of the proposed edge deletion technique as a basis for approximate inference.
Cite
Text
Darwiche et al. "On Bayesian Network Approximation by Edge Deletion." Conference on Uncertainty in Artificial Intelligence, 2005.Markdown
[Darwiche et al. "On Bayesian Network Approximation by Edge Deletion." Conference on Uncertainty in Artificial Intelligence, 2005.](https://mlanthology.org/uai/2005/darwiche2005uai-bayesian/)BibTeX
@inproceedings{darwiche2005uai-bayesian,
title = {{On Bayesian Network Approximation by Edge Deletion}},
author = {Darwiche, Adnan and Chan, Hei and Choi, Arthur},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2005},
pages = {128-135},
url = {https://mlanthology.org/uai/2005/darwiche2005uai-bayesian/}
}