The text is divided into three parts:
- Part I: Inverse Problems and Estimation
- Part II: Modelling of Random Fields
- Part III: Methods and Algorithms
The parts are designed to be complementary, respectively emphasizing mathematical theory, modeling, and algorithms. Click on the bold chapter headings below to expand / collapse details of the table of contents:
Part 0: Preamble and Introduction
Preamble and Introduction:
Part I: Inverse Problems and Estimation
Chapter 2: Inverse Problems
Chapter 3: Static Estimation and Sampling
Chapter 4: Dynamic Estimation and Sampling
Part II: Modelling of Random Fields
Chapter 5: Multidimensional Modelling
Chapter 6: Markov Random Fields
Chapter 7: Hidden Markov Models
Chapter 8: Changes of Basis
Part III: Methods and Algorithms
Chapter 9: Linear Systems Estimation
Chapter 10: Kalman Filtering and Domain Decomposition
Chapter 11: Sampling and Monte Carlo Methods
Appendices and Postmatter
Appendix A: Algebra
Appendix B: Statistics
Appendix C: Image Processing
Bibliography and Index