Title :
Qualitative-Quantitative Bayesian Belief Networks for reliability and risk assessment
Author :
Wang, Chengdong ; Mosleh, Ali
Author_Institution :
Univ. of Maryland, College Park, MD, USA
Abstract :
This paper presents an extension of Bayesian belief networks (BBN) enabling use of both qualitative and quantitative likelihood scales in inference. The proposed method is accordingly named QQBBN (Qualitative-Quantitative Bayesian Belief Networks). The inclusion of qualitative scales is especially useful when quantitative data for estimation of probabilities are lacking and experts are reluctant to express their opinions quantitatively. In reliability and risk analysis such situation occurs when for example human and organizational root causes of systems are modeled explicitly. Such causes are often not quantifiable due to limitations in the state of the art and lack of proper quantitative metrics. This paper describes the proposed QQBBN framework and demonstrates its uses through a simple example.
Keywords :
belief networks; estimation theory; fault trees; probability; reliability theory; risk analysis; QQBBN; probability estimation; qualitative-quantitative Bayesian belief network; reliability assessment; risk analysis; risk assessment; Application software; Artificial intelligence; Bayesian methods; Clustering algorithms; Humans; Inference algorithms; Power system modeling; Risk analysis; Risk management; Sociotechnical systems; Bayesian Belief Networks; Qualitative; Quantitative; Reliability; Risk;
Conference_Titel :
Reliability and Maintainability Symposium (RAMS), 2010 Proceedings - Annual
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-5102-9
Electronic_ISBN :
0149-144X
DOI :
10.1109/RAMS.2010.5448022