Uncertainty in Artificial Intelligence
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Exploiting Evidence in Probabilistic Inference
Mark Chavira, David Allen, Adnan Darwiche
We define the notion of compiling a Bayesian network with evidence and provide a specific approach for evidence-based compilation, which makes use of logical processing. The approach is practical and advantageous in a number of application areas-including maximum likelihood estimation, sensitivity analysis, and MAP computations-and we provide specific empirical results in the domain of genetic linkage analysis. We also show that the approach is applicable for networks that do not contain determinism, and show that it empirically subsumes the performance of the quickscore algorithm when applied to noisy-or networks.
Pages: 112-119
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PDF Link: /papers/05/p112-chavira.pdf
AUTHOR = "Mark Chavira and David Allen and Adnan Darwiche",
TITLE = "Exploiting Evidence in Probabilistic Inference",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "112--119"

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