Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions
Abstract
Estimating individual probabilities of causation generally requires prior knowledge of causal mechanisms. For traffic accidents such knowledge is often available and supports the discipline of accident reconstruction. In this paper structural knowledge is combined with Bayesian network methods to calculate the probability of necessity due to speeding for each of a set of vehicle/pedestrian collisions. Gibbs sampling is used to carry out the computations.
Cite
Text
Davis. "Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions." Conference on Uncertainty in Artificial Intelligence, 2001.Markdown
[Davis. "Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions." Conference on Uncertainty in Artificial Intelligence, 2001.](https://mlanthology.org/uai/2001/davis2001uai-using/)BibTeX
@inproceedings{davis2001uai-using,
title = {{Using Bayesian Networks to Identify the Causal Effect of Speeding in Individual Vehicle/Pedestrian Collisions}},
author = {Davis, Gary A.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2001},
pages = {105-111},
url = {https://mlanthology.org/uai/2001/davis2001uai-using/}
}