Uncertainty in Artificial Intelligence
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Solving Hybrid Influence Diagrams with Deterministic Variables
Yijing Li, Prakash Shenoy
Abstract:
We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. We describe an extended Shenoy-Shafer architecture for propagation of discrete, continuous, and utility potentials in hybrid influence diagrams that include deterministic chance variables. The algorithm and framework are illustrated by solving two small examples.
Keywords:
Pages: 322-331
PS Link:
PDF Link: /papers/10/p322-li.pdf
BibTex:
@INPROCEEDINGS{Li10,
AUTHOR = "Yijing Li and Prakash Shenoy",
TITLE = "Solving Hybrid Influence Diagrams with Deterministic Variables",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "322--331"
}


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