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Uncertainty in Engineering

Part of the SpringerBriefs in Statistics Book Series

Editors: Aslett, Louis J. M.; Coolen, Frank P. A.; De Bock, Jasper

DOI: 10.1007/978-3-030-83640-5

ISBN: 978-3-030-83639-9

Publisher: Springer International Publishing - Springer Journals

Dec 10, 2021

1 - 9 of 9 Chapters

Available

Introduction to Bayesian Statistical Inference

Karagiannis, Georgios P.

Pages: 1 –13

[We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We present tools for parametric and predictive...
Available

Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers

Aslett, Louis J. M.

Pages: 15 –35

[Models which are constructed to represent the uncertainty arising in engineered systems can often be quite complex to ensure they provide a reasonably faithful reflection of the real-world system. As a result, even computation of simple expectations, event probabilities, variances, or...
Available

Introduction to the Theory of Imprecise Probability

Quaeghebeur, Erik

Pages: 37 –50

[The theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a...
Available

Imprecise Discrete-Time Markov Chains

de Cooman, Gert

Pages: 51 –65

[I present a short and easy introduction to a number of basic definitions and important results from the theory of imprecise Markov chains in discrete time, with a finite state space. The approach is intuitive and graphical.]
Available

Statistics with Imprecise Probabilities—A Short Survey

Augustin, Thomas

Pages: 67 –79

[This chapter aims at surveying and highlighting in an introductory way some challenges and big opportunities a paradigmatic shift to imprecise probabilities could induce in statistical modelling. Working with an informal understanding of imprecise probabilities, we discuss the concepts of model...
Available

Reliability

Jackson, Lisa; Coolen, Frank P. A.

Pages: 81 –94

[This chapter introduces key concepts for quantification of system reliability. In addition, basics of statistical inference for reliability data are explained, in particular, the derivation of the likelihood function.]
Available

Simulation Methods for the Analysis of Complex Systems

George-Williams, Hindolo; Santhosh, T. V.; Patelli, Edoardo

Pages: 95 –113

[Everyday systems like communication, transportation, energy and industrial systems are an indispensable part of our daily lives. Several methods have been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they...
Available

Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment

Bi, Sifeng; Beer, Michael

Pages: 115 –129

[This chapter presents the technique route of model updating in the presence of imprecise probabilities. The emphasis is put on the inevitable uncertainties, in both numerical simulations and experimental measurements, leading the updating methodology to be significantly extended from...
Available

Aerospace Flight Modeling and Experimental Testing

Chazot, Olivier

Pages: 131 –147

[Validation processes for aerospace flight modeling require to articulate uncertainty quantification methods with the experimental approach. On this note, the specific strategies for the reproduction of re-entry flow conditions in ground-based facilities are reviewed. It shows how it combines...

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