Localized partial evaluation of belief networks

Authors:
Denise L. Draper;Steve Hanks
Affiliations:
Department of Computer Science and Engineering, University of Washington, Seattle, WA;Department of Computer Science and Engineering, University of Washington, Seattle, WA
Venue:
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Year:
1994

Citing 12
Cited 4

Bayesian and non-Bayesian evidential updating

Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
An introduction to possibilistic and fuzzy logics

Readings in uncertain reasoning
A model for reasoning about persistence and causation

Computational Intelligence
Initialization for the method of conditioning in Bayesian belief networks

Artificial Intelligence
Fusion and propagation with multiple observations in belief networks

Artificial Intelligence
Towards precision of probabilistic bounds propagation

UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Modeling a dynamic and uncertain world I: symbolic and probabilistic reasoning about change

Artificial Intelligence
An algorithm for probabilistic planning

Artificial Intelligence - Special volume on planning and scheduling
Interval Influence Diagrams

UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Average-case analysis of a search algorithm for estimating prior and posterior probabilities in Bayesian networks with extreme probabilities

IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1

Overlap pattern synthesis with an efficient nearest neighbor classifier

Pattern Recognition
Some experiments with real-time decision algorithms

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review
Importance sampling algorithms for Bayesian networks: Principles and performance

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (point-valued) marginal probability for every node in the network. Often, however, an application will not need information about every node in the network nor will it need exact probabilities. We present the localized partial evaluation (LPE) propagation algorithm, which computes interval bounds on the marginal probability of a specified query node by examining a subset of the nodes in the entire network. Conceptually, LPE ignores parts of the network that are "too far away" from the queried node to have much impact on its value. LPE has the "anytime" property of being able to produce better solutions (tighter intervals) given more time to consider more of the network.