Approximate Inference for Infinite Contingent Bayesian Networks
Abstract
In many practical problems---from tracking aircraft based on radar data to building a bibliographic database based on citation lists---we want to reason about an unbounded number of unseen objects with unknown relations among them. Bayesian networks, which define a fixed dependency structure on a finite set of variables, are not the ideal representation language for this task. This paper introduces contingent Bayesian networks (CBNs), which represent uncertainty about dependencies by labeling each edge with a condition under which it is active. A CBN may contain cycles and have infinitely many variables.
Cite
Text
Milch et al. "Approximate Inference for Infinite Contingent Bayesian Networks." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.Markdown
[Milch et al. "Approximate Inference for Infinite Contingent Bayesian Networks." Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.](https://mlanthology.org/aistats/2005/milch2005aistats-approximate/)BibTeX
@inproceedings{milch2005aistats-approximate,
title = {{Approximate Inference for Infinite Contingent Bayesian Networks}},
author = {Milch, Brian and Marthi, Bhaskara and Sontag, David and Russell, Stuart and Ong, Daniel L. and Kolobov, Andrey},
booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
year = {2005},
pages = {238-245},
volume = {R5},
url = {https://mlanthology.org/aistats/2005/milch2005aistats-approximate/}
}