Uncertainty in Engineering

1. Front Matter

   Pages i-vii

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2. Introduction to Bayesian Statistical Inference

   • Georgios P. Karagiannis

   Pages 1-13Open Access

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

   • Louis J. M. Aslett

   Pages 15-35Open Access

4. Introduction to the Theory of Imprecise Probability

   • Erik Quaeghebeur

   Pages 37-50Open Access

5. Imprecise Discrete-Time Markov Chains

   • Gert de Cooman

   Pages 51-65Open Access

6. Statistics with Imprecise Probabilities—A Short Survey

   • Thomas Augustin

   Pages 67-79Open Access

7. Reliability

   • Lisa Jackson, Frank P. A. Coolen

   Pages 81-94Open Access

8. Simulation Methods for the Analysis of Complex Systems

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

   Pages 95-113Open Access

9. Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment

   • Sifeng Bi, Michael Beer

   Pages 115-129Open Access

10. Aerospace Flight Modeling and Experimental Testing

    • Olivier Chazot

    Pages 131-147Open Access

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.

• Uncertainty quantification
• Engineering applications
• Imprecise Probabilities
• Bayesian Statistics
• Markov Chains
• Reliability
• Complex systems
• Inconsistent information
• Model validation
• Experimental measurements
• Open Access

• Department of Mathematical Sciences, Durham University, Durham, UK

  Louis J. M. Aslett, Frank P. A. Coolen

• Foundations Lab for imprecise probabilities, Ghent University, Zwijnaarde, Belgium

  Jasper De Bock

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.