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

Author

Wang, Hua-Wei ; Zhou, Jing-lun ; Zu-Yu He ; Sha, Ji-Chang

Author_Institution

Sch. of Humanities & Manage., Nat. Univ. of Defense Technol., Changsha, China

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

968

Abstract

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.

Keywords

belief networks; inference mechanisms; large-scale systems; learning (artificial intelligence); probability; reliability; uncertainty handling; Bayesian belief networks; agile modeling; complex system; parameter learning; probability distribution; reasoning; reliability growth analysis; structure learning; uncertainty handling; Bayesian methods; Electronic mail; Helium; Information analysis; Probability distribution; Random variables; Reliability; Technology management; Testing; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on

Print_ISBN

0-7803-7508-4

Type

conf

DOI

10.1109/ICMLC.2002.1174527

Filename

1174527