Bayesian inference in probabilistic risk assessment-The current state of the art

被引:172
作者
Kelly, Dana L. [1 ]
Smith, Curtis L. [1 ]
机构
[1] Idaho Natl Lab, Idaho Falls, ID 83415 USA
关键词
Bayesian inference; Markov chain Monte Carlo; Parameter estimation; Model validation; Probabilistic risk analysis; HIGHER-LEVEL; UNCERTAINTY; COUNTS; TIME;
D O I
10.1016/j.ress.2008.07.002
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Markov chain Monte Carlo (MCMC) approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, including probabilistic risk analysis. The advent of freely available software coupled with inexpensive computing power has catalyzed this advance. This paper examines where the risk assessment community is with respect to implementing modern computational-based Bayesian approaches to inference. Through a series of examples in different topical areas, it introduces salient concepts and illustrates the practical application of Bayesian inference via MCMC sampling to a variety of important problems. (c) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:628 / 643
页数:16
相关论文
共 54 条
[1]  
Akaike H., 1992, Breakthroughs in Statistics, P610, DOI [10.1007/978-1-4612-0919-538, DOI 10.1007/978-1-4612-0919-538, DOI 10.1007/978-1-4612-1694-0_15]
[2]  
ANDREW BL, 2006, STAT METHODS SPATIAL
[3]  
Andrew G., 2004, BAYESIAN DATA ANAL, Vthird
[4]  
ANDREW G, 1996, INFERENCE MONITORING, P131
[5]  
ANDREW G, 1996, STAR SIN, P733
[6]  
[Anonymous], 2003, NUREGCR6823 US NUCL
[7]  
Apostolakis G., 1981, Reliability Engineering, V2, P135, DOI 10.1016/0143-8174(81)90019-6
[8]   THE CONCEPT OF PROBABILITY IN SAFETY ASSESSMENTS OF TECHNOLOGICAL SYSTEMS [J].
APOSTOLAKIS, G .
SCIENCE, 1990, 250 (4986) :1359-1364
[9]  
Ascher H., 1984, REPAIRABLE SYSTEMS R
[10]  
ATWOOD CL, 1997, NUREGCR5496 US NUCL