Details
| Original language | English |
|---|---|
| Title of host publication | Studies in Computational Intelligence |
| Publisher | Springer Verlag |
| Pages | 78-85 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-319-73150-6 |
| ISBN (print) | 978-3-319-73149-0 |
| Publication status | Published - 20 Dec 2017 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 760 |
| ISSN (Print) | 1860-949X |
Abstract
It is well know how to estimate the uncertainty of the result y of data processing if we know the correlations between all the inputs. Sometimes, however, we have no information about the correlations. In this case, instead of a single value σ of the standard deviation of the result, we get a range [σ̲,σ¯] of possible values. In this paper, we show how to compute this range.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
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Studies in Computational Intelligence. Springer Verlag, 2017. p. 78-85 (Studies in Computational Intelligence; Vol. 760).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - What if we do not know correlations?
AU - Neumann, Ingo
AU - Beer, Michael
AU - Gong, Zitong
AU - Sriboonchitta, Songsak
AU - Kreinovich, Vladik
N1 - Funding information: This work was also supported in part by the US National Science Foundation grant HRD-1242122. Acknowledgments. We acknowledge the partial support of the Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand. This work was performed when Vladik was a visiting researcher with the Geodetic Institute of the Leibniz University of Hannover, a visit supported by the German Science Foundation.
PY - 2017/12/20
Y1 - 2017/12/20
N2 - It is well know how to estimate the uncertainty of the result y of data processing if we know the correlations between all the inputs. Sometimes, however, we have no information about the correlations. In this case, instead of a single value σ of the standard deviation of the result, we get a range [σ̲,σ¯] of possible values. In this paper, we show how to compute this range.
AB - It is well know how to estimate the uncertainty of the result y of data processing if we know the correlations between all the inputs. Sometimes, however, we have no information about the correlations. In this case, instead of a single value σ of the standard deviation of the result, we get a range [σ̲,σ¯] of possible values. In this paper, we show how to compute this range.
UR - http://www.scopus.com/inward/record.url?scp=85038842719&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73150-6_5
DO - 10.1007/978-3-319-73150-6_5
M3 - Contribution to book/anthology
AN - SCOPUS:85038842719
SN - 978-3-319-73149-0
T3 - Studies in Computational Intelligence
SP - 78
EP - 85
BT - Studies in Computational Intelligence
PB - Springer Verlag
ER -