Uncertainty Quantification
Theory, Implementation, and Applications
Second Edition

Ralph C. Smith

This book was published by SIAM in the Computational Science and Engineering Series, CS30, 2024.

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If you have constructive comments or corrections, please contact me at rsmith@ncsu.edu.


List of Corrections to First Printing:


MA 540: Uncertainty Quantification for Physical and Biological Models, which uses this book:

Table of Contents

Preface
 
Chapter 1. Introduction
   
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PART I: APPLICATIONS AND MODELS

Chapter 2. Applications

Chapter 3. Models and Data


PART II: PROBABILITY AND STATISTICS CONCEPTS

Chapter 4. Topics from Probability and Statistics
   
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Chapter 5. Representation of Random Parameters and Fields
   
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Chapter 6. Observation Models
   
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PART III: PARAMETER SELECTION TECHNIQUES

Chapter 7. Parameter Identifiability and Influence
  
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Chapter 8. Local Sensitivity Analysis
   
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Chapter 9. Global Sensitivity Analysis
   
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Chapter 10. Active Subspace Analysis
   
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PART IV: INVERSE AND FORWARD UNCERTAINTY QUANTIFICATION
 
Chapter 11. Frequentist Parameter Interference
   
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Chapter 12. Bayesian Parameter Inference
   
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Chapter 13. Uncertainty Propagation for Model Responses
 
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Chapter 14. Model Discrepancy
   
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PART V: SURROGATE AND REDUCED-ORDER MODELS

Chapter 15. Surrogate Models
   
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Chapter 16. Numerical Surrogate Models

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Chapter 17. Spectral Surrogates for Differential Equations

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Chapter 18. Statistical Surrogate Models

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Chapter 19. Reduced-Order Models
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Chapter 20. Numerical and Statistical Integration Techniques
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Appendix A. Supporting Material