Details
| Original language | English |
|---|---|
| Title of host publication | Transportation Systems; Safety Engineering, Risk Analysis and Reliability Methods; Applied Stochastic Optimization, Uncertainty and Probability |
| Publisher | American Society of Mechanical Engineers(ASME) |
| Pages | 821-829 |
| Number of pages | 9 |
| ISBN (print) | 9780791854952 |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | ASME 2011 International Mechanical Engineering Congress and Exposition (IMECE 2011) - Denver, Denver, CO, United States Duration: 11 Nov 2011 → 17 Nov 2011 |
Publication series
| Name | ASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011 |
|---|---|
| Volume | 9 |
Abstract
A realistic quantification of all input information is a basic requirement in order to obtain useful results from engineering analyses. The concept of quantification and the associated uncertainty model have to be selected in agreement with the amount and quality of the available information. For inconsistent information, a distinction between probabilistic and nonprobabilistic characteristics is beneficial. In this distinction, uncertainty refers to probabilistic characteristics and nonprobabilistic characteristics are summarized as imprecision. When uncertainty and imprecision occur simultaneously, the uncertainty model fuzzy randomness appears useful. In this paper, the fuzzy probabilistic model is utilized in a Bayesian approach to take account of imprecision in data and in prior expert knowledge. The propagation of imprecision and uncertainty is investigated for selected cases. The Bayesian approach extended to inconsistent information is demonstrated by means of an example.
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
Cite this
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Transportation Systems; Safety Engineering, Risk Analysis and Reliability Methods; Applied Stochastic Optimization, Uncertainty and Probability. American Society of Mechanical Engineers(ASME), 2011. p. 821-829 (ASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011; Vol. 9).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Bayesian update with fuzzy information
AU - Beer, Michael
AU - Stein, Matthias
PY - 2011
Y1 - 2011
N2 - A realistic quantification of all input information is a basic requirement in order to obtain useful results from engineering analyses. The concept of quantification and the associated uncertainty model have to be selected in agreement with the amount and quality of the available information. For inconsistent information, a distinction between probabilistic and nonprobabilistic characteristics is beneficial. In this distinction, uncertainty refers to probabilistic characteristics and nonprobabilistic characteristics are summarized as imprecision. When uncertainty and imprecision occur simultaneously, the uncertainty model fuzzy randomness appears useful. In this paper, the fuzzy probabilistic model is utilized in a Bayesian approach to take account of imprecision in data and in prior expert knowledge. The propagation of imprecision and uncertainty is investigated for selected cases. The Bayesian approach extended to inconsistent information is demonstrated by means of an example.
AB - A realistic quantification of all input information is a basic requirement in order to obtain useful results from engineering analyses. The concept of quantification and the associated uncertainty model have to be selected in agreement with the amount and quality of the available information. For inconsistent information, a distinction between probabilistic and nonprobabilistic characteristics is beneficial. In this distinction, uncertainty refers to probabilistic characteristics and nonprobabilistic characteristics are summarized as imprecision. When uncertainty and imprecision occur simultaneously, the uncertainty model fuzzy randomness appears useful. In this paper, the fuzzy probabilistic model is utilized in a Bayesian approach to take account of imprecision in data and in prior expert knowledge. The propagation of imprecision and uncertainty is investigated for selected cases. The Bayesian approach extended to inconsistent information is demonstrated by means of an example.
UR - http://www.scopus.com/inward/record.url?scp=84869177278&partnerID=8YFLogxK
U2 - 10.1115/imece2011-62424
DO - 10.1115/imece2011-62424
M3 - Conference contribution
AN - SCOPUS:84869177278
SN - 9780791854952
T3 - ASME 2011 International Mechanical Engineering Congress and Exposition, IMECE 2011
SP - 821
EP - 829
BT - Transportation Systems; Safety Engineering, Risk Analysis and Reliability Methods; Applied Stochastic Optimization, Uncertainty and Probability
PB - American Society of Mechanical Engineers(ASME)
T2 - ASME 2011 International Mechanical Engineering Congress and Exposition (IMECE 2011)
Y2 - 11 November 2011 through 17 November 2011
ER -