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The Uncertainty analysis of model re...

Hofer, Eduard.

The Uncertainty analysis of model resultsa practical guide /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Uncertainty analysis of model resultsby Eduard Hofer.
其他題名:	a practical guide /
作者:	Hofer, Eduard.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xv, 346 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Statistics.
電子資源:	http://dx.doi.org/10.1007/978-3-319-76297-5
ISBN:	9783319762975$q(electronic bk.)

The Uncertainty analysis of model resultsa practical guide /
Hofer, Eduard.

The Uncertainty analysis of model resultsa practical guide /[electronic resource] :by Eduard Hofer. - Cham :Springer International Publishing :2018. - xv, 346 p. :ill., digital ;24 cm.

Preface -- Introduction and necessary distinctions -- Step 1: Search -- Step 2: Quantify -- Step 3: Propagate -- Step 4: Estimate uncertainty -- Step 5: Rank uncertainties -- Step 6: Present the analysis and interpret its results -- Practical execution of the analysis -- Uncertainty analysis when separation of uncertainties is required -- Practical examples -- References -- Subject index.

This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.

ISBN: 9783319762975$q(electronic bk.)

Standard No.: 10.1007/978-3-319-76297-5doiSubjects--Topical Terms:

182057
Statistics.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

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520 $a This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.
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