Uncertainty
Quantification
Theory, Implementation, and Applications
Ralph C. Smith
This book was published by SIAM in the Computational Science and Engineering Series, CS12, 2014.
Click here to order the book.
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 uses this book.
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
Link
to MATLAB codes
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
Link to MATLAB codes
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
