Name
Abstract | ||
|---|---|---|
Uncertainty quantification is an important approach to modeling in the presence of limited information about uncertain quantities. As a result recent years have witnessed a burgeoning body of work in this field. The present paper gives some background, highlights some recent work, and presents some problems and challenges. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1023/A:1025888503247 | Reliable Computing |
Keywords | Field | DocType |
uncertainty quantification | Mathematical optimization,Dependability,Uncertainty quantification,Industrial engineering,Operations research,Quantization (signal processing),Perfect information,Uncertainty handling,Mathematics,Complete information | Journal |
Volume | Issue | ISSN |
9 | 6 | 1573-1340 |
Citations | PageRank | References |
8 | 0.73 | 7 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Berleant | 1 | 448 | 52.40 |
| Mei-Peng Cheong | 2 | 8 | 0.73 |
| Chris C. N. Chu | 3 | 275 | 21.03 |
| Yong Guan | 4 | 787 | 82.67 |
| Ahmed E. Kamal | 5 | 2068 | 147.26 |
| Gerald Shedblé | 6 | 8 | 0.73 |
| Scott Ferson | 7 | 305 | 37.30 |
| James F. Peters | 8 | 1825 | 184.11 |