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

Abstract

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.

Authors:
 [1];  [2];  [1];  [3];  [3];  [1];  [1];  [4]
  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)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1427286
Report Number(s):
SAND-2015-4167J
Journal ID: ISSN 9999-0014; 590253
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article
Journal Name:
Sandia journal manuscript; Not yet accepted for publication
Additional Journal Information:
Journal Name: Sandia journal manuscript; Not yet accepted for publication; Journal ID: ISSN 9999-0014
Publisher:
Sandia
Country of Publication:
United States
Language:
English

Citation Formats

Ferson, Scott, Nelsen, Roger B., Hajagos, Janos, Berleant, Daniel J., Zhang, Jianzhong, Tucker, W. Troy, Ginzburg, Lev R., and Oberkampf, William L. Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis. United States: N. p., 2015. Web.
Ferson, Scott, Nelsen, Roger B., Hajagos, Janos, Berleant, Daniel J., Zhang, Jianzhong, Tucker, W. Troy, Ginzburg, Lev R., & Oberkampf, William L. Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis. United States.
Ferson, Scott, Nelsen, Roger B., Hajagos, Janos, Berleant, Daniel J., Zhang, Jianzhong, Tucker, W. Troy, Ginzburg, Lev R., and Oberkampf, William L. 2015. "Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis". United States. https://www.osti.gov/servlets/purl/1427286.
@article{osti_1427286,
title = {Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis},
author = {Ferson, Scott and Nelsen, Roger B. and Hajagos, Janos and Berleant, Daniel J. and Zhang, Jianzhong and Tucker, W. Troy and Ginzburg, Lev R. and Oberkampf, William L.},
abstractNote = {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.},
doi = {},
url = {https://www.osti.gov/biblio/1427286}, journal = {Sandia journal manuscript; Not yet accepted for publication},
issn = {9999-0014},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {5}
}