Efficient indexing methods for recursive decompositions of Bayesian networks

Authors:
Kevin Grant
Affiliations:
Department of Math and Computer Science, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta, Canada T1K 3M4
Venue:
International Journal of Approximate Reasoning
Year:
2012

Citing 7
Cited 0

The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
Recursive conditioning

Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Operating with potentials of discrete variables

International Journal of Approximate Reasoning
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning

Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A standard approach for optimizing belief network inference using query DAGs

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Topological parameters for time-space tradeoff

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Conditioning graphs: practical structures for inference in bayesian networks

AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider efficient indexing methods for conditioning graphs, which are a form of recursive decomposition for Bayesian networks. We compare two well-known methods for indexing, a top-down method and a bottom-up method, and discuss the redundancy that each of these suffer from. We present a new method for indexing that combines the advantages of each model in order to reduce this redundancy. We also introduce the concept of an update manager, which is a node in the conditioning graph that controls when other nodes update their current index. Empirical evaluations over a suite of standard test networks show a considerable reduction both in the amount of indexing computation that takes place, and the overall runtime required by the query algorithm.