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

Journal Article · Fri May 01 00:00:00 EDT 2015 · Sandia journal manuscript; Not yet accepted for publication

OSTI ID:1427286

Ferson, Scott ^[1]; Nelsen, Roger B. ^[2]; Hajagos, Janos ^[1]; Berleant, Daniel J. ^[3]; Zhang, Jianzhong ^[3]; Tucker, W. Troy ^[1]; Ginzburg, Lev R. ^[1]; Oberkampf, William L. ^[4]

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1. Applied Biomathematics, Setauket, NY (United States)
2. Lewis & Clark College, Portland OR (United States)
3. Iowa State Univ., Ames, IA (United States)
4. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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 Lab. (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1427286
• Report Number(s): SAND-2015-4167J; 590253
• Journal Information: Sandia journal manuscript; Not yet accepted for publication, Journal Name: Sandia journal manuscript; Not yet accepted for publication; ISSN 9999-0014
• Publisher: Sandia
• Country of Publication: United States
• Language: English