Aster, Richard C.
Parameter estimation and inverse problems - 3rd ed. - Amsterdam Elsevier c2019 - xi, 392p.; 24cm.
Table of contents
1. Introduction
2. Linear Regression
3. Rank Deficiency and Ill-Conditioning
4. Tikhonov Regularization
5. Discretizing by Basis Functions
6. Iterative Methods of Solving Linear Problems
7. Additional Regularization Techniques
8. Fourier Techniques
9. Nonlinear Regression
10. Nonlinear Inverse Problems
11. Bayesian Methods
12 Adjoint Methods
Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more.
9780128046517
Mathematics
Analysis
Differential calculus
Differential equations
515.357 AST/P
Parameter estimation and inverse problems - 3rd ed. - Amsterdam Elsevier c2019 - xi, 392p.; 24cm.
Table of contents
1. Introduction
2. Linear Regression
3. Rank Deficiency and Ill-Conditioning
4. Tikhonov Regularization
5. Discretizing by Basis Functions
6. Iterative Methods of Solving Linear Problems
7. Additional Regularization Techniques
8. Fourier Techniques
9. Nonlinear Regression
10. Nonlinear Inverse Problems
11. Bayesian Methods
12 Adjoint Methods
Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more.
9780128046517
Mathematics
Analysis
Differential calculus
Differential equations
515.357 AST/P