How to Gauge a Combination of Uncertainties of Different Type: General Foundations

Research output: Chapter in book/report/conference proceedingContribution to book/anthologyResearchpeer review

Authors

Research Organisations

External Research Organisations

  • University of Texas at El Paso
  • Banking University of Ho Chi Minh City
View graph of relations

Details

Original languageEnglish
Title of host publicationBehavioral Predictive Modeling in Economics
PublisherSpringer Nature Switzerland AG
Pages195-201
Number of pages7
ISBN (electronic)978-3-030-49728-6
ISBN (print)978-3-030-49727-9
Publication statusPublished - 6 Aug 2020

Publication series

NameStudies in Computational Intelligence
Volume897
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

Cite this

How to Gauge a Combination of Uncertainties of Different Type: General Foundations. / Neumann, Ingo; Kreinovich, Vladik; Nguyen, Thach Ngoc.
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 proceedingContribution to book/anthologyResearchpeer review

Neumann, I, Kreinovich, V & Nguyen, TN 2020, How to Gauge a Combination of Uncertainties of Different Type: General Foundations. in Behavioral Predictive Modeling in Economics. Studies in Computational Intelligence, vol. 897, Springer Nature Switzerland AG, pp. 195-201. https://doi.org/10.1007/978-3-030-49728-6_13
Neumann, I., Kreinovich, V., & Nguyen, T. N. (2020). How to Gauge a Combination of Uncertainties of Different Type: General Foundations. In Behavioral Predictive Modeling in Economics (pp. 195-201). (Studies in Computational Intelligence; Vol. 897). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-49728-6_13
Neumann I, Kreinovich V, Nguyen TN. How to Gauge a Combination of Uncertainties of Different Type: General Foundations. In Behavioral Predictive Modeling in Economics. Springer Nature Switzerland AG. 2020. p. 195-201. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-49728-6_13
Neumann, Ingo ; Kreinovich, Vladik ; Nguyen, Thach Ngoc. / How to Gauge a Combination of Uncertainties of Different Type : General Foundations. Behavioral Predictive Modeling in Economics. Springer Nature Switzerland AG, 2020. pp. 195-201 (Studies in Computational Intelligence).
Download
@inbook{1ed9b737f8b346b99833a2316cd88959,
title = "How to Gauge a Combination of Uncertainties of Different Type: General Foundations",
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.",
author = "Ingo Neumann and Vladik Kreinovich and Nguyen, {Thach Ngoc}",
note = "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.",
year = "2020",
month = aug,
day = "6",
doi = "10.1007/978-3-030-49728-6_13",
language = "English",
isbn = "978-3-030-49727-9",
series = "Studies in Computational Intelligence",
publisher = "Springer Nature Switzerland AG",
pages = "195--201",
booktitle = "Behavioral Predictive Modeling in Economics",
address = "Switzerland",

}

Download

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 -

By the same author(s)