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ORCIDReliability Engineering & System Safety, Volume 85
Volume 85, Numbers 1-3, July - September 2004
- Jon C. Helton, William Oberkampf:
Alternative representations of epistemic uncertainty. 1-10 - William Oberkampf, Jon C. Helton, Cliff A. Joslyn, Steven F. Wojtkiewicz, Scott Ferson
:
Challenge problems: uncertainty in system response given uncertain parameters. 11-19 - George J. Klir:
Generalized information theory: aims, results, and open problems. 21-38 - Jon C. Helton, Jay D. Johnson, William Oberkampf:
An exploration of alternative approaches to the representation of uncertainty in model predictions. 39-71 - Thomas Fetz, Michael Oberguggenberger:
Propagation of uncertainty through multivariate functions in the framework of sets of probability measures. 73-87 - Jim W. Hall, Jonathan Lawry:
Generation, combination and extension of random set approximations to coherent lower and upper probabilities. 89-101 - Igor Kozine, Lev V. Utkin:
An approach to combining unreliable pieces of evidence and their propagation in a system response analysis. 103-112 - Gert de Cooman, Matthias C. M. Troffaes:
Coherent lower previsions in systems modelling: products and aggregation rules. 113-134 - Scott Ferson, Janos G. Hajagos:
Arithmetic with uncertain numbers: rigorous and (often) best possible answers. 135-152 - Daniel Berleant, Jianzhong Zhang:
Representation and problem solving with Distribution Envelope Determination (DEnv). 153-168 - Fulvio Tonon:
Using random set theory to propagate epistemic uncertainty through a mechanical system. 169-181 - John R. Red-Horse, Allan S. Benjamin:
A probabilistic approach to uncertainty quantification with limited information. 183-190 - Weiye Li, James Mac Hyman:
Computer arithmetic for probability distribution variables. 191-209 - Brian Rutherford:
A response-modeling approach to characterization and propagation of uncertainty specified over intervals. 211-222 - Bilal M. Ayyub:
From dissecting ignorance to solving algebraic problems. 223-238 - Anthony O'Hagan, Jeremy E. Oakley:
Probability is perfect, but we can't elicit it perfectly. 239-248 - Yakov Ben-Haim:
Uncertainty, probability and information-gaps. 249-266 - Vladik Kreinovich, Scott Ferson:
A new Cauchy-based black-box technique for uncertainty in risk analysis. 267-279 - Harish Agarwal, John E. Renaud, Evan L. Preston, Dhanesh Padmanabhan:
Uncertainty quantification using evidence theory in multidisciplinary design optimization. 281-294 - Prabhu Soundappan, Efstratios Nikolaidis, Raphael T. Haftka, Ramana V. Grandhi, Robert A. Canfield:
Comparison of evidence theory and Bayesian theory for uncertainty modeling. 295-311 - Roger M. Cooke:
The anatomy of the squizzel: The role of operational definitions in representing uncertainty. 313-319 - Vicki M. Bier:
Implications of the research on expert overconfidence and dependence. 321-329 - Jane M. Booker, Laura A. McNamara:
Solving black box computation problems using expert knowledge theory and methods. 331-340 - Ronald R. Yager:
Uncertainty modeling and decision support. 341-354 - Scott Ferson, Cliff A. Joslyn, Jon C. Helton, William Oberkampf, Kari Sentz:
Summary from the epistemic uncertainty workshop: consensus amid diversity. 355-369
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