Uncertainty in engineering : introduction to methods and applications / Louis J.M. Aslett, Frank P.A. Coolen, Jasper De Bock, editors.
Aslett, Louis J. M., editor.; Coolen, Franciscus Petrus Antonius, 1965- editor.; De Bock, Jasper, editor.
2022
TA340 .U53 2022
Available Online

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Title
Uncertainty in engineering : introduction to methods and applications / Louis J.M. Aslett, Frank P.A. Coolen, Jasper De Bock, editors.
ISBN
9783030836405 (electronic bk.)
3030836401 (electronic bk.)
9783030836399
DOI
https://doi.org/10.1007/978-3-030-83640-5
Published
Cham : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (148 pages) : illustrations (chiefly color)
Item Number
10.1007/978-3-030-83640-5 doi
Call Number
TA340 .U53 2022
Dewey Decimal Classification
519.5
Summary
This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Open access
Source of Description
Description based on print version record.
Added Author
Aslett, Louis J. M., editor.
Coolen, Franciscus Petrus Antonius, 1965- editor.
De Bock, Jasper, editor.
Series
SpringerBriefs in statistics.
Available in Other Form
Uncertainty in Engineering.
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All Resources
Introduction to Bayesian statistical inference
Sampling from complex probability distributions: a Monte Carlo primer for engineers
Introduction to the theory of imprecise probability
Imprecise discrete-time Markov chains
Statistics with imprecise probabilities-a short survey
Reliability
Simulation methods for the analysis of complex systems
Overview of stochastic model updating in aerospace application under uncertainty treatment
Aerospace flight modeling and experimental testing.

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