Agile Bayesian belief networks and application on complex system reliability growth analysis

Agile Bayesian belief networks and application on complex system reliability growth analysis

Bayesian belief networks (BBN) provide an effective way of reasoning under uncertainty and diverse source information. BBN have a wide application of uncertainty modeling. With the application being more complex and dynamic, the modeling of BBN needs to be flexible and agile. In this paper, we have...

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Bibliographic Details
Published in:Proceedings. International Conference on Machine Learning and Cybernetics Vol. 2; pp. 968 - 972 vol.2
Main Authors: Hua-Wei Wang, Jing-Lun Zhou, Zu-Yu He, Ji-Chang Sha
Format: Conference Proceeding
Language:English
Published: IEEE 2002
Subjects:
Bayesian methods
Electronic mail
Helium
Information analysis
Probability distribution
Random variables
Reliability
Technology management
Testing
Uncertainty
Online Access:Get full text
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Summary:Bayesian belief networks (BBN) provide an effective way of reasoning under uncertainty and diverse source information. BBN have a wide application of uncertainty modeling. With the application being more complex and dynamic, the modeling of BBN needs to be flexible and agile. In this paper, we have developed an improved BBN, called agile BBN, which emphasizes the structure and parameter learning of the model. An example is presented of using the agile BBN for a complex system reliability growth analysis.
ISBN:9780780375086
0780375084
DOI:10.1109/ICMLC.2002.1174527