A fully Bayesian approach for combining multi-level information in multi-state fault tree quantification

被引:43
作者
Graves, T. L.
Hamada, M. S.
Klamann, R.
Koehler, A.
Martz, H. F.
机构
[1] Los Alamos Natl Lab, Grp CCS 6, Los Alamos, NM 87545 USA
[2] Los Alamos Natl Lab, Los Alamos, NM USA
关键词
Dirichlet distribution; Markov chain Monte Carlo;
D O I
10.1016/j.ress.2006.11.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents a fully Bayesian approach that simultaneously combines non-overlapping (in time) basic event and higher-level event failure data in fault tree quantification with multi-state events. Such higher-level data often correspond to train, subsystem or system failure events. The fully Bayesian approach also automatically propagates the highest-level data to lower levels in the fault tree. A simple example illustrates our approach. (C) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1476 / 1483
页数:8
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