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Uncertainty in Engineering: Introduction to Methods and Applications de Louis J. M. Aslett

Uncertainty in Engineering: Introduction to Methods and Applications: SpringerBriefs in Statistics

Editat de Louis J. M. Aslett, Frank P. A. Coolen, Jasper De Bock
en Limba Engleză Paperback – 10 dec 2021
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.
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Specificații

ISBN-13: 9783030836399
ISBN-10: 3030836398
Pagini: 147
Ilustrații: VII, 147 p. 55 illus., 41 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.23 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Statistics

Locul publicării:Cham, Switzerland

Cuprins

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.

Notă biografică

Louis Aslett is an Associate Professor in the Department of Mathematical Sciences at Durham University.  His main research interests span applied work in statistical machine learning and computational statistics, as well as methodological work in reliability theory and at the interface between cryptography and statistics. Threaded through this work is an interest in developing these statistical methods so that they are amenable to implementation in modern high performance computing architectures.
Frank Coolen is a Professor in the Department of Mathematical Sciences at Durham University. His main research activities are in the theory and methods of statistics and reliability. He has developed methods in nonparametric predictive inference, a frequentist statistics methodology based on few assumptions made possible through the use of imprecise probability for uncertainty quantification. He has presented the survival signature as a powerful tool for system reliability.He has been on the editorial boards of several journals, including the Journal of Statistical Theory and Practice, Communications in Statistics, and the Journal of Risk and Reliability. 
Jasper De Bock is an Assistant Professor in the Department of Electronics and Information Systems at Ghent University. He is also the current president of SIPTA  (the Society for Imprecise Probabilities: Theories and Applications). His research is concerned with the foundations of imprecise probabilities and their application to robust inference and decision making, with a particular focus on imprecise stochastic processes and choice functions.














Textul de pe ultima copertă

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.

Caracteristici

Discusses reliability theory and simulation methods for complex systems Gives an introduction to uncertainty quantifcation in engineering Illustrated with applications in aerospace flight modelling