Survey of sampling-based methods for uncertainty and sensitivity analysis

被引:839
作者
Helton, J. C.
Johnson, J. D.
Sallaberry, C. J.
Storlie, C. B.
机构
[1] Sandia Natl Labs, Dept 6849, Albuquerque, NM 87185 USA
[2] Arizona State Univ, Dept Math & Stat, Tempe, AZ 85287 USA
[3] ProStat, Mesa, AZ 85204 USA
[4] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
关键词
aleatory uncertainty; epistemic uncertainty; Latin hypercube sampling; Monte Carlo; sensitivity analysis; uncertainty analysis;
D O I
10.1016/j.ress.2005.11.017
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (ii) generation of samples from uncertain analysis inputs, (iii) propagation of sampled inputs through an analysis, (iv) presentation of uncertainty analysis results, and (v) determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two-dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1175 / 1209
页数:35
相关论文
共 211 条
[61]  
Garvey JE, 1998, ECOLOGY, V79, P442
[62]   IMPORTANCE SAMPLING FOR STOCHASTIC SIMULATIONS [J].
GLYNN, PW ;
IGLEHART, DL .
MANAGEMENT SCIENCE, 1989, 35 (11) :1367-1392
[63]  
Goossens L. H. J., 1998, Journal of Radiological Protection, V18, P249, DOI 10.1088/0952-4746/18/4/003
[64]   Expert judgement for a probabilistic accident consequence uncertainty analysis [J].
Goossens, LHJ ;
Harper, FT ;
Kraan, BCP ;
Métivier, H .
RADIATION PROTECTION DOSIMETRY, 2000, 90 (03) :295-301
[65]   A UNIFIED FRAMEWORK FOR SIMULATING MARKOVIAN MODELS OF HIGHLY DEPENDABLE SYSTEMS [J].
GOYAL, A ;
SHAHABUDDIN, P ;
HEIDELBERGER, P ;
NICOLA, VF ;
GLYNN, PW .
IEEE TRANSACTIONS ON COMPUTERS, 1992, 41 (01) :36-51
[66]   THE NUREG-1150 PROBABILISTIC RISK ASSESSMENT FOR THE SEQUOYAH-NUCLEAR-PLANT [J].
GREGORY, JJ ;
BREEDING, RJ ;
HELTON, JC ;
MURFIN, WB ;
HIGGINS, SJ ;
SHIVER, AW .
NUCLEAR ENGINEERING AND DESIGN, 1992, 135 (01) :95-115
[67]  
GRIEWANK A, 2000, EVALUATION DERIVATIV
[68]   Subsurface stormflow modeling with sensitivity analysis using a Latin-hypercube sampling technique [J].
Gwo, JP ;
Toran, LE ;
Morris, MD ;
Wilson, GV .
GROUND WATER, 1996, 34 (05) :811-818
[69]  
HAAN CT, 1989, T ASAE, V32, P137, DOI 10.13031/2013.30973
[70]  
Halpern J.Y., 2003, REASONING UNCERTAINT