Uncertainty Quantification
Theory, Implementation, and Applications

Ralph C. Smith

This book was published by SIAM in the Computational Science and Engineering Series, CS12, 2014.

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


List of Corrections to First Printing: Download PDF File


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

Table of Contents

Preface
 
Chapter 1. Introduction

Chapter 2. Large-Scale Applications

Chapter 3. Prototypical Models

Chapter 4. Fundamentals of Probability, Random Processes, and Statistics

Chapter 5. Representation of Random Inputs

Chapter 6. Parameter Selection Techniques

Chapter 7. Frequentist Techniques for Parameter Estimation
   
Link to MATLAB codes and heat data

Chapter 8. Bayesian Techniques for Parameter Estimation
    Link to MATLAB codes and synthetic HIV data

Chapter 9. Uncertainty Propagation in Models
   
Link to MATLAB codes and synthetic HIV data

Chapter 10. Stochastic Spectral Methods

Chapter 11. Sparse Grid Quadrature and Interpolation Techniques

Chapter 12. Prediction in the Presence of Model Discrepancy
   
Link to MATLAB codes and heat data

Chapter 13. Surrogate Models

Chapter 14. Local Sensitivity Analysis

Chapter 15. Global Sensitivity Analysis

Appendix A. Concepts from Functional Analysis