Efficient Search-Based Inference for Noisy-or Belief Networks: TopEpsilon
Abstract
Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been shown to be more efficient. In this paper, we present a search-based algorithm for approximate inference on arbitrary, noisy-OR belief networks, generalizing earlier work on search-based inference for two-level, noisy-OR belief networks. Initial experimental results appear promising.
Cite
Text
Huang and Henrion. "Efficient Search-Based Inference for Noisy-or Belief Networks: TopEpsilon." Conference on Uncertainty in Artificial Intelligence, 1996.Markdown
[Huang and Henrion. "Efficient Search-Based Inference for Noisy-or Belief Networks: TopEpsilon." Conference on Uncertainty in Artificial Intelligence, 1996.](https://mlanthology.org/uai/1996/huang1996uai-efficient/)BibTeX
@inproceedings{huang1996uai-efficient,
title = {{Efficient Search-Based Inference for Noisy-or Belief Networks: TopEpsilon}},
author = {Huang, Kurt and Henrion, Max},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1996},
pages = {325-331},
url = {https://mlanthology.org/uai/1996/huang1996uai-efficient/}
}