Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions

Van Nguyen
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:101-110, 2017.

Abstract

We propose a new message-passing belief propa- gation method that approximates belief updating on evidential networks with conditional belief functions. By means of local conditioning, the method is able to propagate beliefs on the origi- nal multiply-connected network structure using local computations, facilitating reasoning in a distributed and dynamic context. Further, by use of conditional belief functions in the form of par- tially defined plausibility and basic plausibility assignment functions, belief updating can be ef- ficiently approximated using only partial infor- mation of the belief functions involved. Exper- iments show that the method produces results with high degree of accuracy whilst achieving a significant decrease in computational and space complexity (compared to exact methods).

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-nguyen17a, title = {Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions}, author = {Nguyen, Van}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {101--110}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/nguyen17a/nguyen17a.pdf}, url = {https://proceedings.mlr.press/r15/nguyen17a.html}, abstract = {We propose a new message-passing belief propa- gation method that approximates belief updating on evidential networks with conditional belief functions. By means of local conditioning, the method is able to propagate beliefs on the origi- nal multiply-connected network structure using local computations, facilitating reasoning in a distributed and dynamic context. Further, by use of conditional belief functions in the form of par- tially defined plausibility and basic plausibility assignment functions, belief updating can be ef- ficiently approximated using only partial infor- mation of the belief functions involved. Exper- iments show that the method produces results with high degree of accuracy whilst achieving a significant decrease in computational and space complexity (compared to exact methods).}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions %A Van Nguyen %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-nguyen17a %I PMLR %P 101--110 %U https://proceedings.mlr.press/r15/nguyen17a.html %V R15 %X We propose a new message-passing belief propa- gation method that approximates belief updating on evidential networks with conditional belief functions. By means of local conditioning, the method is able to propagate beliefs on the origi- nal multiply-connected network structure using local computations, facilitating reasoning in a distributed and dynamic context. Further, by use of conditional belief functions in the form of par- tially defined plausibility and basic plausibility assignment functions, belief updating can be ef- ficiently approximated using only partial infor- mation of the belief functions involved. Exper- iments show that the method produces results with high degree of accuracy whilst achieving a significant decrease in computational and space complexity (compared to exact methods). %Z Reissued by PMLR on 04 October 2026.
APA
Nguyen, V.. (2017). Approximate Evidential Reasoning Using Local Conditioning and Conditional Belief Functions. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:101-110 Available from https://proceedings.mlr.press/r15/nguyen17a.html. Reissued by PMLR on 04 October 2026.

Related Material