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

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) ; Hajagos, Janos (Applied Biomathematics, Setauket, NY) ; Nelsen, Roger B. (Lewis & ; Clark College, Portland, OR)

Sandia National Laboratories

关键词: Probabilities.;    Probability;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Computerized Simulation;    Distribution Functions;   

DOI  :  10.2172/919189
RP-ID  :  SAND2004-3072
RP-ID  :  AC04-94AL85000
RP-ID  :  919189

美国|英语

来源: UNT Digital Library

【 摘 要 】

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.