Uncertainty in Artificial Intelligence
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On the Robustness of Most Probable Explanations
Hei Chan, Adnan Darwiche
Abstract:
In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with a highest probability given the current evidence. In this paper, we discuss the problem of finding robustness conditions of the MPE under single parameter changes. Specifically, we ask the question: How much change in a single network parameter can we afford to apply while keeping the MPE unchanged? We will describe a procedure, which is the first of its kind, that computes this answer for each parameter in the Bayesian network variable in time O(n exp(w)), where n is the number of network variables and w is its treewidth.
Keywords:
Pages: 63-71
PS Link:
PDF Link: /papers/06/p63-chan.pdf
BibTex:
@INPROCEEDINGS{Chan06,
AUTHOR = "Hei Chan and Adnan Darwiche",
TITLE = "On the Robustness of Most Probable Explanations",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "63--71"
}


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