Details
| Original language | English |
|---|---|
| Title of host publication | Behavioral Predictive Modeling in Economics |
| Publisher | Springer Nature Switzerland AG |
| Pages | 195-201 |
| Number of pages | 7 |
| ISBN (Electronic) | 978-3-030-49728-6 |
| ISBN (Print) | 978-3-030-49727-9 |
| Publication status | Published - 6 Aug 2020 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 897 |
| ISSN (Print) | 1860-949X |
| ISSN (Electronic) | 1860-9503 |
Abstract
In many practical situations, for some components of the uncertainty (e.g., of the measurement error) we know the corresponding probability distribution, while for other components, we know only upper bound on the corresponding values. To decide which of the algorithms or techniques leads to less uncertainty, we need to be able to gauge the combined uncertainty by a single numerical value—so that we can select the algorithm for which this values is the best. There exist several techniques for gauging the combination of interval and probabilistic uncertainty. In this paper, we consider the problem of gauging the combination of different types of uncertainty from the general fundamental viewpoint. As a result, we develop a general formula for such gauging—a formula whose particular cases include the currently used techniques.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
Cite this
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- BibTeX
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Behavioral Predictive Modeling in Economics. Springer Nature Switzerland AG, 2020. p. 195-201 (Studies in Computational Intelligence; Vol. 897).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - How to Gauge a Combination of Uncertainties of Different Type
T2 - General Foundations
AU - Neumann, Ingo
AU - Kreinovich, Vladik
AU - Nguyen, Thach Ngoc
N1 - Funding Information: This work was supported by the Institute of Geodesy, Leibniz University of Hannover. It was also supported in part by the US National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science) and HRD-1242122 (Cyber-ShARE Center of Excellence). This paper was written when V. Kreinovich was visiting Leibniz University of Hannover.
PY - 2020/8/6
Y1 - 2020/8/6
N2 - In many practical situations, for some components of the uncertainty (e.g., of the measurement error) we know the corresponding probability distribution, while for other components, we know only upper bound on the corresponding values. To decide which of the algorithms or techniques leads to less uncertainty, we need to be able to gauge the combined uncertainty by a single numerical value—so that we can select the algorithm for which this values is the best. There exist several techniques for gauging the combination of interval and probabilistic uncertainty. In this paper, we consider the problem of gauging the combination of different types of uncertainty from the general fundamental viewpoint. As a result, we develop a general formula for such gauging—a formula whose particular cases include the currently used techniques.
AB - In many practical situations, for some components of the uncertainty (e.g., of the measurement error) we know the corresponding probability distribution, while for other components, we know only upper bound on the corresponding values. To decide which of the algorithms or techniques leads to less uncertainty, we need to be able to gauge the combined uncertainty by a single numerical value—so that we can select the algorithm for which this values is the best. There exist several techniques for gauging the combination of interval and probabilistic uncertainty. In this paper, we consider the problem of gauging the combination of different types of uncertainty from the general fundamental viewpoint. As a result, we develop a general formula for such gauging—a formula whose particular cases include the currently used techniques.
UR - http://www.scopus.com/inward/record.url?scp=85089885469&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49728-6_13
DO - 10.1007/978-3-030-49728-6_13
M3 - Contribution to book/anthology
AN - SCOPUS:85089885469
SN - 978-3-030-49727-9
T3 - Studies in Computational Intelligence
SP - 195
EP - 201
BT - Behavioral Predictive Modeling in Economics
PB - Springer Nature Switzerland AG
ER -