Probabilistic Inference in Influence Diagrams
Abstract
This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems that are as easy to solve as possible. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988; Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the new method are much easier to solve than those induced by the two previous methods.
Cite
Text
Zhang. "Probabilistic Inference in Influence Diagrams." Conference on Uncertainty in Artificial Intelligence, 1998. doi:10.1111/0824-7935.00073Markdown
[Zhang. "Probabilistic Inference in Influence Diagrams." Conference on Uncertainty in Artificial Intelligence, 1998.](https://mlanthology.org/uai/1998/zhang1998uai-probabilistic/) doi:10.1111/0824-7935.00073BibTeX
@inproceedings{zhang1998uai-probabilistic,
title = {{Probabilistic Inference in Influence Diagrams}},
author = {Zhang, Nevin Lianwen},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1998},
pages = {514-522},
doi = {10.1111/0824-7935.00073},
url = {https://mlanthology.org/uai/1998/zhang1998uai-probabilistic/}
}