Logarithmic-Time Updates and Queries in Probabilistic Networks

Arthur L. Delcher, Adam J. Grove, Simon Kasif, Judea Pearl

JAIR 1996 pp. 37-59

doi:10.1613/JAIR.238 /jair/1996/delcher1996jair-logarithmictime/

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 algorithm, new evidence is 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(log 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.

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Text

Delcher et al. "Logarithmic-Time Updates and Queries in Probabilistic Networks." Journal of Artificial Intelligence Research, 1996. doi:10.1613/JAIR.238

Markdown

[Delcher et al. "Logarithmic-Time Updates and Queries in Probabilistic Networks." Journal of Artificial Intelligence Research, 1996.](https://mlanthology.org/jair/1996/delcher1996jair-logarithmictime/) doi:10.1613/JAIR.238

BibTeX

@article{delcher1996jair-logarithmictime,
  title     = {{Logarithmic-Time Updates and Queries in Probabilistic Networks}},
  author    = {Delcher, Arthur L. and Grove, Adam J. and Kasif, Simon and Pearl, Judea},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {1996},
  pages     = {37-59},
  doi       = {10.1613/JAIR.238},
  volume    = {4},
  url       = {https://mlanthology.org/jair/1996/delcher1996jair-logarithmictime/}
}