Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis.

Technical Report · Fri Oct 01 04:00:00 EDT 2004

DOI:https://doi.org/10.2172/919189· OSTI ID:919189

Oberkampf, William Louis; Tucker, W Troy ^[1]; Zhang, Jianzhong ^[2]; Ginzburg, Lev ^[1]; Berleant, Daniel J ^[2]; Ferson, Scott ^[1]; Hajagos, Janos ^[1]; Nelsen, Roger B ^[3]

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1. Applied Biomathematics, Setauket, NY
2. Iowa State University, Ames, IA
3. Lewis & Clark College, Portland, OR

This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models.

• Research Organization: Sandia National Laboratories
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 919189
• Report Number(s): SAND2004-3072
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
99 GENERAL AND MISCELLANEOUS
BOUNDARY CONDITIONS
COMPUTERIZED SIMULATION
DISTRIBUTION FUNCTIONS
PROBABILITY
Probabilistic risk analysis
Probabilities.
RISK ASSESSMENT
Risk assessment.
Spain.
Technology-Risk assessment.