An Importance Sampling Algorithm Based on Evidence Pre-Propagation
Abstract
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function using two techniques: loopy belief propagation [19, 25] and e-cutoff heuristic [2]. We tested the performance of EPIS-BN on three large real Bayesian networks: ANDES [3], CPCS [21], and PATHFINDER[11]. We observed that on each of these networks the EPIS-BN algorithm outperforms AISBN [2], the current state of the art algorithm, while avoiding its costly learning stage.
Cite
Text
Yuan and Druzdzel. "An Importance Sampling Algorithm Based on Evidence Pre-Propagation." Conference on Uncertainty in Artificial Intelligence, 2003.Markdown
[Yuan and Druzdzel. "An Importance Sampling Algorithm Based on Evidence Pre-Propagation." Conference on Uncertainty in Artificial Intelligence, 2003.](https://mlanthology.org/uai/2003/yuan2003uai-importance/)BibTeX
@inproceedings{yuan2003uai-importance,
title = {{An Importance Sampling Algorithm Based on Evidence Pre-Propagation}},
author = {Yuan, Changhe and Druzdzel, Marek J.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2003},
pages = {624-631},
url = {https://mlanthology.org/uai/2003/yuan2003uai-importance/}
}