A Monte Carlo framework for probabilistic analysis and variance decomposition with distribution parameter uncertainty

被引:26
|
作者
McFarland, John [1 ]
DeCarlo, Erin [1 ]
机构
[1] Southwest Res Inst, 6220 Culebra Rd, San Antonio, TX 78238 USA
关键词
Probabilistic analysis; Uncertainty quantification; Epistemic uncertainty; Monte Carlo; Sensitivity analysis; Variance decomposition; 1996 PERFORMANCE ASSESSMENT; GLOBAL SENSITIVITY-ANALYSIS; QUANTIFICATION CHALLENGE; STRUCTURAL RELIABILITY; BAYESIAN-INFERENCE; MODELS; INDEXES; METHODOLOGY; PROPAGATION; VARIABILITY;
D O I
10.1016/j.ress.2020.106807
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Probabilistic methods are used with modeling and simulation to predict variation in system performance and assess risk due to randomness in model inputs such as material properties, loads, and boundary conditions. It is common practice to assume that the input distributions are known. However, this discounts the epistemic uncertainty in the values of the distribution parameters, which can be attributed to the availability of limited data to define the input distributions. This paper proposes a Monte Carlo framework for unified treatment of both aleatory and epistemic uncertainty types when assessing system performance and risk. A Bayesian philosophy is adopted, whereby epistemic uncertainty is characterized using probability theory. Several computational approaches are outlined for propagation and sensitivity analysis with distribution parameter uncertainty. As a result of the outlined framework, the overall influence of epistemic uncertainties can be quantified in terms of confidence bounds on statistical quantities such as failure probability, and the relative influence of each source of epistemic uncertainty is quantified using variance decomposition. The proposed methods are demonstrated using both an analytical example and a fatigue crack growth analysis.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] VARIANCE REDUCTION AND ROBUST PROCEDURES IN MONTE-CARLO ANALYSIS
    GENTLE, JE
    OPERATIONS RESEARCH, 1975, 23 : B415 - B415
  • [32] Brief Overview of Methods for Measurement Uncertainty Analysis: GUM Uncertainty Framework, Monte Carlo Method, Characteristic Function Approach
    Witkovsky, V.
    Wimmer, G.
    Durisova, Z.
    Duris, S.
    Palencar, R.
    2017 11TH INTERNATIONAL CONFERENCE ON MEASUREMENT, 2017, : 35 - 38
  • [33] A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications
    Clements, Kayla B.
    Geraci, Gianluca
    Olson, Aaron J.
    Palmer, Todd S.
    JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER, 2024, 319
  • [34] Topological analysis in Monte Carlo simulation for uncertainty propagation
    Pakyuz-Charrier, Evren
    Jessell, Mark
    Giraud, Jeremie
    Lindsay, Mark
    Ogarko, Vitaliy
    SOLID EARTH, 2019, 10 (05) : 1663 - 1684
  • [35] Uncertainty Analysis for Fluorescence Tomography with Monte Carlo Method
    Reinbacher-Koestinger, Alice
    Freiberger, Manuel
    Scharfetter, Hermann
    DIFFUSE OPTICAL IMAGING III, 2011, 8088
  • [36] Monte Carlo probabilistic sensitivity analysis for patient level simulation models: Efficient estimation of mean and variance using ANOVA
    O'Hagan, Anthony
    Stevenson, Matt
    Madan, Jason
    HEALTH ECONOMICS, 2007, 16 (10) : 1009 - 1023
  • [37] Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm
    Kuczera, G
    Parent, E
    JOURNAL OF HYDROLOGY, 1998, 211 (1-4) : 69 - 85
  • [38] PARAMETER SENSITIVITIES, MONTE-CARLO FILTERING, AND MODEL FORECASTING UNDER UNCERTAINTY
    ROSE, KA
    SMITH, EP
    GARDNER, RH
    BRENKERT, AL
    BARTELL, SM
    JOURNAL OF FORECASTING, 1991, 10 (1-2) : 117 - 133
  • [39] Hybrid probabilistic interval analysis of bar structures with uncertainty using a mixed perturbation Monte-Carlo method
    Gao, Wei
    Wu, Di
    Song, Chongmin
    Tin-Loi, Francis
    Li, Xiaojing
    FINITE ELEMENTS IN ANALYSIS AND DESIGN, 2011, 47 (07) : 643 - 652
  • [40] MONTE-CARLO COMPARISONS OF PARAMETER ESTIMATORS OF 2-PARAMETER WEIBULL DISTRIBUTION
    GROSS, AJ
    LURIE, D
    IEEE TRANSACTIONS ON RELIABILITY, 1977, 26 (05) : 356 - 358