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From Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability: Practice-Motivated Approach to Measurement Planning and Data Processing

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1174))

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Abstract

When we process data, it is important to take into account that data comes with uncertainty. There exist techniques for quantifying uncertainty and propagating this uncertainty through the data processing algorithms. However, most of these techniques do not take into account that in th real world, measuring instruments are not 100% reliable – they sometimes malfunction and produce values which are far off from the measured values of the corresponding quantities. How can we take into account both uncertainty and reliability? In this paper, we consider several possible scenarios, and we show, for each scenario, what is the natural way to plan the measurements and to quantify and propagate the resulting uncertainty and reliability.

This work was supported in part by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Focus Program SPP 100+ 2388, Grant Nr. 501624329, by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), HRD-1834620 and HRD-2034030 (CAHSI Includes), EAR-2225395 (Center for Collective Impact in Earthquake Science C-CIES), and by the AT&T Fellowship in Information Technology.

It was also supported by a grant from the Hungarian National Research, Development and Innovation Office (NRDI).

The authors are greatly thankful to the anonymous referees for valuable suggestions.

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Author information

Authors and Affiliations

  1. Leibniz University Hannover, Hannover, Germany

    Niklas R. Winnewisser & Michael Beer

  2. University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich & Olga Kosheleva

Authors
  1. Niklas R. Winnewisser
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  2. Michael Beer
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  3. Vladik Kreinovich
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  4. Olga Kosheleva
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Corresponding author

Correspondence to Vladik Kreinovich .

Editor information

Editors and Affiliations

  1. LIP6, BC169, Sorbonne Université, Paris, France

    Marie-Jeanne Lesot

  2. Departamento de Engenharia Mecânica, University of Lisbon, Lisbon, Portugal

    Susana Vieira

  3. Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Marek Z. Reformat

  4. INESC-ID,Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    João Paulo Carvalho

  5. INESC-ID, Av. das Forças Armadas, 1649-026, ISCTE - University Institute of Lisbon, Lisboa, Portugal

    Fernando Batista

  6. LIP6, BC169, Place Jussieu 4, 75005, Sorbonne University, Paris, France

    Bernadette Bouchon-Meunier

  7. Machine Intelligence Institute, 715 North Avenue, Iona Collage, NEW ROCHELLE, NY, USA

    Ronald R. Yager

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Winnewisser, N.R., Beer, M., Kreinovich, V., Kosheleva, O. (2024). From Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability: Practice-Motivated Approach to Measurement Planning and Data Processing. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2024. Lecture Notes in Networks and Systems, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-031-74003-9_31

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