Titlebook: Uncertainty in Engineering; Introduction to Meth Louis J. M. Aslett,Frank P. A. Coolen,Jasper De Bo Book‘‘‘‘‘‘‘‘ 2022 The Editor(s) (if app

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Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers, faithful reflection of the real-world system. As a result, even computation of simple expectations, event probabilities, variances, or integration over utilities for a decision problem can be analytically intractable. Indeed, such models are often sufficiently high dimensional that even traditional

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,Statistics with?Imprecise Probabilities—A Short Survey,lities could induce in statistical modelling. Working with an informal understanding of imprecise probabilities, we discuss the concepts of model imprecision and data imprecision as the two main types of imprecision in statistical modelling. Then we provide a short survey of some major developments,

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,Simulation Methods for?the?Analysis of?Complex Systems,been developed for their reliability assessment—while analytical methods are computationally more efficient and often yield exact solutions, they are unable to account for the structural and functional complexities of these systems. These complexities often require the analyst to make unrealistic as

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,Overview of?Stochastic Model Updating in?Aerospace Application Under Uncertainty Treatment,nties, in both numerical simulations and experimental measurements, leading the updating methodology to be significantly extended from deterministic sense to stochastic sense. This extension requires that the model parameters are not regarded as unknown-but-fixed values, but random variables with un

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Book‘‘‘‘‘‘‘‘ 2022nte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The ?nal two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective,

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