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Erschienen in:
Assessment of Sustainability is Essential for Performability Evaluation Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

29. Simplified Analysis of Incomplete Data on Risk

verfasst von : Bernhard Reer

Erschienen in: Handbook of Advanced Performability Engineering

Verlag: Springer International Publishing

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Abstract

A framework for simplified analysis of incomplete data on risk is presented and illustrated in five feasibility case studies on road traffic and occupational safety. The Bayesian theorem is utilized for both structuring cases of incomplete input data and providing options for dealing with incompleteness. The application of the framework requires the availability of an interval scale of an index of prevention in a situation exposed to failure. A key parameter of the framework is bounded in the range from 0 to 1 and represents the average degree of prevention (v) in failure exposure situations for a given type of risk. The Bayesian structure of the framework allows to verify an expert judgement for v by a quantitative evaluation of failure events only meaning a quantitative evaluation of the variety of failure exposure situations would not be necessary. Moreover, non-trivial comparisons between different types of risks are possible. The loss of accuracy identified from the case studies is assessed as a not satisfactory result. It is an open issue whether such inaccuracy is inherent, when applying a common and simple model for data analysis addressing various risk environments, or it can be reduced by a refined version of a common model or by improved scaling.

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Vorheriges Kapitel The Neglected Pillar of Science: Risk and Uncertainty Analysis
Nächstes Kapitel Combining Domain-Independent Methods and Domain-Specific Knowledge to Achieve Effective Risk and Uncertainty Reduction
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Metadaten
Titel
Simplified Analysis of Incomplete Data on Risk
verfasst von
Bernhard Reer
Copyright-Jahr
2021
Buch
Handbook of Advanced Performability Engineering

Print ISBN: 978-3-030-55731-7

Electronic ISBN: 978-3-030-55732-4

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-55732-4_29

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