Logarithmic-Time Updates and Queries in Probabilistic Networks
Artificial Intelligence
2014-08-08 v1
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.
Cite
@article{arxiv.1408.1479,
title = {Logarithmic-Time Updates and Queries in Probabilistic Networks},
author = {Arthur L. Delcher and Adam J. Grove and Simon Kasif and Judea Pearl},
journal= {arXiv preprint arXiv:1408.1479},
year = {2014}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)