Backward Simulation in Bayesian Networks
Abstract
Backward simulation is an approximate inference technique for Bayesian belief networks. It differs from existing simulation methods in that it starts simulation from the known evidence and works backward (i.e., contrary to the direction of the arcs). The technique's focus on the evidence leads to improved convergence in situations where the posterior beliefs are dominated by the evidence rather than by the prior probabilities. Since this class of situations is large, the technique may make practical the application of approximate inference in Bayesian belief networks to many real-world problems.
Cite
Text
Fung and Del Favero. "Backward Simulation in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/B978-1-55860-332-5.50034-1Markdown
[Fung and Del Favero. "Backward Simulation in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/fung1994uai-backward/) doi:10.1016/B978-1-55860-332-5.50034-1BibTeX
@inproceedings{fung1994uai-backward,
title = {{Backward Simulation in Bayesian Networks}},
author = {Fung, Robert M. and Del Favero, Brendan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1994},
pages = {227-234},
doi = {10.1016/B978-1-55860-332-5.50034-1},
url = {https://mlanthology.org/uai/1994/fung1994uai-backward/}
}