Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Uncertainty quantification in computational sciences and engineering |
| Untertitel | UNCECOMP 2015 : proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering : held in Crete, Greece, 25-27 May 2015 |
| Erscheinungsort | Athens |
| Seiten | 908-923 |
| Seitenumfang | 16 |
| ISBN (elektronisch) | 9789609999496 |
| Publikationsstatus | Veröffentlicht - 2015 |
| Extern publiziert | Ja |
| Veranstaltung | 1st ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2015 - Hersonissos, Crete, Großbritannien / Vereinigtes Königreich Dauer: 25 Mai 2015 → 27 Mai 2015 |
Abstract
A computational framework for the reduction and computation of Bayesian Networks enhanced with structural reliability methods is presented. During the last decades, the inner flexibility of the Bayesian Network method, its intuitive graphical structure and the strong mathematical background have attracted increasing interest in a large variety of applications involving joint probability of complex events and dependencies. Furthermore, the fast growing availability of computational power on the one side and the implementation of robust inference algorithms on the other, have additionally promoted the success of this method. Inference in Bayesian Networks is limited to only discrete variables (with the only exception of Gaussian distributions) in case of exact algorithms, whereas approximate approach allows to handle continuous distributions but can either result computationally inefficient or have unknown rates of convergence. This work provides a valid alternative to the traditional approach without renouncing to the reliability and robustness of exact inference computation. The methodology adopted is based on the combination of Bayesian Networks with structural reliability methods and allows to integrate random and interval variables within the Bayesian Network framework in the so called Enhanced Bayesian Networks. In the following, the computational algorithms developed are described and a simple structural application is proposed in order to fully show the capability of the tool developed.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Theoretische Informatik und Mathematik
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Uncertainty quantification in computational sciences and engineering: UNCECOMP 2015 : proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering : held in Crete, Greece, 25-27 May 2015. Athens, 2015. S. 908-923.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A computational tool for Bayesian networks enhanced with reliability methods
AU - Tolo, Silvia
AU - Patelli, Edoardo
AU - Beer, Michael
AU - Kang, Zhan
PY - 2015
Y1 - 2015
N2 - A computational framework for the reduction and computation of Bayesian Networks enhanced with structural reliability methods is presented. During the last decades, the inner flexibility of the Bayesian Network method, its intuitive graphical structure and the strong mathematical background have attracted increasing interest in a large variety of applications involving joint probability of complex events and dependencies. Furthermore, the fast growing availability of computational power on the one side and the implementation of robust inference algorithms on the other, have additionally promoted the success of this method. Inference in Bayesian Networks is limited to only discrete variables (with the only exception of Gaussian distributions) in case of exact algorithms, whereas approximate approach allows to handle continuous distributions but can either result computationally inefficient or have unknown rates of convergence. This work provides a valid alternative to the traditional approach without renouncing to the reliability and robustness of exact inference computation. The methodology adopted is based on the combination of Bayesian Networks with structural reliability methods and allows to integrate random and interval variables within the Bayesian Network framework in the so called Enhanced Bayesian Networks. In the following, the computational algorithms developed are described and a simple structural application is proposed in order to fully show the capability of the tool developed.
AB - A computational framework for the reduction and computation of Bayesian Networks enhanced with structural reliability methods is presented. During the last decades, the inner flexibility of the Bayesian Network method, its intuitive graphical structure and the strong mathematical background have attracted increasing interest in a large variety of applications involving joint probability of complex events and dependencies. Furthermore, the fast growing availability of computational power on the one side and the implementation of robust inference algorithms on the other, have additionally promoted the success of this method. Inference in Bayesian Networks is limited to only discrete variables (with the only exception of Gaussian distributions) in case of exact algorithms, whereas approximate approach allows to handle continuous distributions but can either result computationally inefficient or have unknown rates of convergence. This work provides a valid alternative to the traditional approach without renouncing to the reliability and robustness of exact inference computation. The methodology adopted is based on the combination of Bayesian Networks with structural reliability methods and allows to integrate random and interval variables within the Bayesian Network framework in the so called Enhanced Bayesian Networks. In the following, the computational algorithms developed are described and a simple structural application is proposed in order to fully show the capability of the tool developed.
KW - Bayesian Network
KW - Convex Models
KW - Intervals
KW - OpenCossan
KW - Structural Reliability
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84942868133&partnerID=8YFLogxK
U2 - 10.7712/120215.4316.546
DO - 10.7712/120215.4316.546
M3 - Conference contribution
AN - SCOPUS:84942868133
SP - 908
EP - 923
BT - Uncertainty quantification in computational sciences and engineering
CY - Athens
T2 - 1st ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2015
Y2 - 25 May 2015 through 27 May 2015
ER -