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

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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.

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153 p.

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Oberkampf, William Louis; Tucker, W. Troy (Applied Biomathematics, Setauket, NY); Zhang, Jianzhong (Iowa State University, Ames, IA); Ginzburg, Lev (Applied Biomathematics, Setauket, NY); Berleant, Daniel J. (Iowa State University, Ames, IA); Ferson, Scott (Applied Biomathematics, Setauket, NY) et al. October 1, 2004.

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Description

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.

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153 p.

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  • Report No.: SAND2004-3072
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/919189 | External Link
  • Office of Scientific & Technical Information Report Number: 919189
  • Archival Resource Key: ark:/67531/metadc884996

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  • October 1, 2004

Added to The UNT Digital Library

  • Sept. 22, 2016, 2:13 a.m.

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  • Nov. 23, 2016, 12:41 p.m.

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Oberkampf, William Louis; Tucker, W. Troy (Applied Biomathematics, Setauket, NY); Zhang, Jianzhong (Iowa State University, Ames, IA); Ginzburg, Lev (Applied Biomathematics, Setauket, NY); Berleant, Daniel J. (Iowa State University, Ames, IA); Ferson, Scott (Applied Biomathematics, Setauket, NY) et al. Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis., report, October 1, 2004; United States. (digital.library.unt.edu/ark:/67531/metadc884996/: accessed October 19, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.