Evidential Reasoning with Conditional Belief Functions
Abstract
In the existing evidential networks with belief functions, the relations among the variables are always represented by joint belief functions on the product space of the involved variables. In this paper, we use conditional belief functions to represent such relations in the network and show some relations of these two kinds of representations. We also present a propagation algorithm for such networks. By analyzing the properties of some special evidential networks with conditional belief functions, we show that the reasoning process can be simplified in such kinds of networks.
Cite
Text
Xu and Smets. "Evidential Reasoning with Conditional Belief Functions." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/B978-1-55860-332-5.50081-XMarkdown
[Xu and Smets. "Evidential Reasoning with Conditional Belief Functions." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/xu1994uai-evidential/) doi:10.1016/B978-1-55860-332-5.50081-XBibTeX
@inproceedings{xu1994uai-evidential,
title = {{Evidential Reasoning with Conditional Belief Functions}},
author = {Xu, Hong and Smets, Philippe},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1994},
pages = {598-605},
doi = {10.1016/B978-1-55860-332-5.50081-X},
url = {https://mlanthology.org/uai/1994/xu1994uai-evidential/}
}