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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
Hierarchical Junction Trees as the Secondary Structure for Inference in Bayesian Networks
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Dan Wu, Libing Wu, "Hierarchical Junction Trees as the Secondary Structure for Inference in Bayesian Networks," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 3, pp. 706-712, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.461,
author = {Dan Wu and Libing Wu},
title = {Hierarchical Junction Trees as the Secondary Structure for Inference in Bayesian Networks},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {3},
year = {2007},
isbn = {0-7695-2909-7},
pages = {706-712},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.461},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - Hierarchical Junction Trees as the Secondary Structure for Inference in Bayesian Networks
SN - 0-7695-2909-7
SP706
EP712
A1 - Dan Wu,
A1 - Libing Wu,
PY - 2007
KW - null
VL - 3
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Dan Wu, University of Windsor, Canada
Libing Wu, Wuhan University, China
Traditionally, a single junction tree is used as the secondary structure for inference in a Bayesian network. However, its applicability and efficiency are restricted by the size of the junction tree. In this paper, we demonstrate that using a hierarchy of junction trees (HJT) as the secondary structure instead will greatly alleviate this restriction and improve the performance. We also compare the proposed HJT with other similar schemes for inference in Bayesian networks.
Citation:
Dan Wu, Libing Wu, "Hierarchical Junction Trees as the Secondary Structure for Inference in Bayesian Networks," snpd, vol. 3, pp.706-712, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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