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Erschienen in:
Introduction and Necessary Distinctions Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

3. STEP 2: Quantify

verfasst von : Eduard Hofer

Erschienen in: The Uncertainty Analysis of Model Results

Verlag: Springer International Publishing

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Abstract

This is the most important and most laborious analysis step. Its task is to express quantitatively what is known about each of the possibly important uncertainties of the computer model application. In other words, the task is to quantify the so-called state of knowledge. Subjective probability is the appropriate mathematical concept for state of knowledge quantifications. Section 3.1 briefly introduces this concept. Section 3.2 explains distinctions between data and model uncertainties that are important in the state of knowledge quantification. Ways to quantify the state of knowledge for uncertain data are presented in Sect. 3.3 while Sect. 3.4 presents those for model uncertainties. The subjects of Sect. 3.5 are the concept and sources of state of knowledge dependence together with ways of its quantification. The elicitation of the state of knowledge from experts and the corresponding probabilistic modelling are discussed in Sect. 3.6 for data as well as for models. Finally, Sect. 3.7 deals with the question of when and how to conduct a survey of expert opinion in order to arrive at state of knowledge quantifications that are supported by a broad base of expertise.

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Vorheriges Kapitel STEP 1: Search
Nächstes Kapitel STEP 3: Propagate
Fußnoten
1
Since sw(A 1 B 1 ) is part of sw(A 1 ) and of sw(B 1 ), it needs to be subtracted.
 
2
Random variables are denoted by capital letters and their realizations by lower case letters.
 
3
Vectors of random variables are denoted by bold capital letters and their realizations by bold lower case letters.
 
4
If the joint distribution is normal, then both marginals are normal but the joint distribution is not necessarily normal if both marginals are normal.
 
5
If his judgment is such that there is no state of knowledge dependence, then he should give 0.5 as his subjective probability. If he judges the data to be completely state of knowledge dependent, then his subjective probability should be 1 (positive complete dependence) or 0 (negative complete dependence).
 
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Metadaten
Titel
STEP 2: Quantify
verfasst von
Eduard Hofer
Copyright-Jahr
2018
Buch
The Uncertainty Analysis of Model Results

Print ISBN: 978-3-319-76296-8

Electronic ISBN: 978-3-319-76297-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-76297-5_3