Uncertainty in Engineering : : Introduction to Methods and Applications.

• Superior document: SpringerBriefs in Statistics Series
• Author: Aslett, Louis J. M.
• TeilnehmendeR: Coolen, Frank P. A.; De Bock, Jasper.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; Ã2022.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Series: SpringerBriefs in Statistics Series
• Physical Description: 1 online resource (148 pages)

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.