Uncertainty in Artificial Intelligence
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Logarithmic-Time Updates and Queries in Probabilistic Networks
Arthur Delcher, Adam Grove, Simon Kasif, Judea Pearl
Abstract:
In this paper we propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks (causal trees and polytrees). In the conventional algorithms, new evidence in absorbed in time O(1) and queries are processed in time O(N), where N is the size of the network. We propose a practical algorithm which, after a preprocessing phase, allows us to answer queries in time O(log N) at the expense of O(logn N) time per evidence absorption. The usefulness of sub-linear processing time manifests itself in applications requiring (near) real-time response over large probabilistic databases.
Keywords:
Pages: 116-124
PS Link:
PDF Link: /papers/95/p116-delcher.pdf
BibTex:
@INPROCEEDINGS{Delcher95,
AUTHOR = "Arthur Delcher and Adam Grove and Simon Kasif and Judea Pearl",
TITLE = "Logarithmic-Time Updates and Queries in Probabilistic Networks",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "116--124"
}


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