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Uncertainty in Engineering : Introduction to Methods and Applications.

Bibliographic Details
Main Author: Aslett, Louis J. M.
Other Authors: Coolen, Frank P. A., De Bock, Jasper.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2021.
Edition:1st ed.
Series:SpringerBriefs in Statistics Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Contents
  • 1 Introduction to Bayesian Statistical Inference
  • 1.1 Introduction
  • 1.2 Specification of the Prior
  • 1.2.1 Conjugate Priors
  • 1.3 Point Estimation
  • 1.4 Credible Sets
  • 1.5 Hypothesis Test
  • 1.5.1 Model Selection
  • References
  • 2 Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers
  • 2.1 Motivation
  • 2.1.1 Generality of Expectations
  • 2.1.2 Why Consider Monte Carlo?
  • 2.2 Monte Carlo Estimators
  • 2.3 Simple Monte Carlo Sampling Methods
  • 2.3.1 Inverse Sampling
  • 2.3.2 Rejection Sampling
  • 2.3.3 Importance Sampling
  • 2.4 Further Reading
  • References
  • 3 Introduction to the Theory of Imprecise Probability
  • 3.1 Introduction
  • 3.2 Fundamental Concepts
  • 3.2.1 Basic Concepts
  • 3.2.2 Coherence
  • 3.3 Previsions and Probabilities
  • 3.3.1 Previsions as Prices for Gambles
  • 3.3.2 Probabilities as Previsions of Indicator Gambles
  • 3.3.3 Assessments of Lower Previsions
  • 3.3.4 Working on Linear Spaces of Gambles
  • 3.4 Sets of Probabilities
  • 3.4.1 From Lower Previsions to Credal Sets
  • 3.4.2 From Credal Sets to Lower Previsions
  • 3.5 Basics of Conditioning
  • 3.6 Remarks About Infinite Possibility Spaces
  • 3.7 Conclusion
  • References
  • 4 Imprecise Discrete-Time Markov Chains
  • 4.1 Introduction
  • 4.2 Precise Probability Models
  • 4.3 Imprecise Probability Models
  • 4.4 Discrete-Time Uncertain Processes
  • 4.5 Imprecise Probability Trees
  • 4.6 Imprecise Markov Chains
  • 4.7 Examples
  • 4.8 A Non-linear Perron-Frobenius Theorem, and Ergodicity
  • 4.9 Conclusion
  • References
  • 5 Statistics with Imprecise Probabilities-A Short Survey
  • 5.1 Introduction
  • 5.2 Some Elementary Background on Imprecise Probabilities
  • 5.3 Types of Imprecision in Statistical Modelling
  • 5.4 Statistical Modelling Under Model Imprecision.
  • 5.4.1 Probabilistic Assumptions on the Sampling Model Matter: Frequentist Statistics and Imprecise Probabilities
  • 5.4.2 Model Imprecision and Generalized Bayesian Inference
  • 5.4.3 Some Other Approaches
  • 5.5 Statistical Modelling Under Data Imprecision
  • 5.6 Concluding Remarks
  • References
  • 6 Reliability
  • 6.1 Introduction
  • 6.2 System Reliability Methods
  • 6.2.1 Fault Tree Analysis
  • 6.2.2 Fault Tree Extensions: Common Cause Failures
  • 6.2.3 Phased Mission Analysis
  • 6.3 Basic Statistical Concepts and Methods for Reliability Data
  • 6.4 Statistical Models for Reliability Data
  • 6.5 Stochastic Processes in Reliability-Models and Inference
  • 7 Simulation Methods for the Analysis of Complex Systems
  • 7.1 Introduction
  • 7.2 Reliability Modelling of Systems and Networks
  • 7.2.1 Traditional Approaches
  • 7.2.2 Interdependencies in Complex Systems
  • 7.3 Load Flow Simulation
  • 7.3.1 Simulation of Interdependent and Reconfigurable Systems
  • 7.3.2 Maintenance Strategy Optimization
  • 7.3.3 Case Study: Station Blackout Risk Assessment
  • 7.4 Survival Signature Simulation
  • 7.4.1 Systems with Imprecision
  • 7.4.2 Case Study: Industrial Water Supply System
  • 7.5 Final Remarks
  • References
  • 8 Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment
  • 8.1 Introduction
  • 8.2 Overview of the State of the Art: Deterministic or Stochastic?
  • 8.3 Overall Technique Route of Stochastic Model Updating
  • 8.3.1 Feature Extraction
  • 8.3.2 Parameter Selection
  • 8.3.3 Surrogate Modelling
  • 8.3.4 Test Analysis Correlation: Uncertainty Quantification Metrics
  • 8.3.5 Model Adjustment and Validation
  • 8.4 Uncertainty Treatment in Parameter Calibration
  • 8.4.1 The Bayesian Updating Framework
  • 8.4.2 A Novel Uncertainty Quantification Metric
  • 8.5 Example: The NASA UQ Challenge.
  • 8.6 Conclusions and Prospects
  • References
  • 9 Aerospace Flight Modeling and Experimental Testing
  • 9.1 Introduction
  • 9.2 Aerospace Flights and Planetary Re-entry
  • 9.3 Similitude Approach for Hypersonic Flows
  • 9.3.1 Inviscid Hypersonics
  • 9.3.2 Viscous Hypersonics
  • 9.3.3 High-Temperature Hypersonics
  • 9.4 Duplication of Dissociated Boundary Layer with Surface Reaction
  • 9.5 Considering Flow Radiation
  • 9.6 Ground Testing Strategy for High-Speed Re-entry
  • 9.7 Conclusion
  • References.

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