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International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP) (12th : 2015)
Compression and inference algorithms for Bayesian network modeling of infrastructure systems Tien, Iris; Der Kiureghian, Armen
Abstract
The Bayesian network (BN) is an ideal tool for modeling and assessing the reliability of civil infrastructure, particularly when the information about the system and its components is uncertain and evolves in time. One of the major limitations of the BN framework, however, is the size and complexity of the system that can be tractably modeled as a BN. This is due to the size of the conditional probability table (CPT) associated with the system node in the BN model, which grows exponentially with the number of components in the system. In this paper, we present novel compression and inference algorithms that utilize compression techniques to achieve significant savings in memory storage of the system CPT. In addition, heuristics developed to improve the computational efficiency of the algorithms are presented. An application to an example system demonstrates the gains in both memory and computation time requirements achieved by the proposed algorithms.
Item Metadata
Title |
Compression and inference algorithms for Bayesian network modeling of infrastructure systems
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Creator | |
Contributor | |
Date Issued |
2015-07
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Description |
The Bayesian network (BN) is an ideal tool for modeling and assessing the reliability of
civil infrastructure, particularly when the information about the system and its components is uncertain
and evolves in time. One of the major limitations of the BN framework, however, is the size and
complexity of the system that can be tractably modeled as a BN. This is due to the size of the
conditional probability table (CPT) associated with the system node in the BN model, which grows
exponentially with the number of components in the system. In this paper, we present novel
compression and inference algorithms that utilize compression techniques to achieve significant
savings in memory storage of the system CPT. In addition, heuristics developed to improve the
computational efficiency of the algorithms are presented. An application to an example system
demonstrates the gains in both memory and computation time requirements achieved by the proposed
algorithms.
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Type | |
Language |
eng
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Notes |
This collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.
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Date Available |
2015-05-22
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivs 2.5 Canada
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DOI |
10.14288/1.0076237
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URI | |
Affiliation | |
Citation |
Haukaas, T. (Ed.) (2015). Proceedings of the 12th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP12), Vancouver, Canada, July 12-15.
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Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivs 2.5 Canada