Parameter Estimation and Inverse Problems (3rd Ed.)
Auteurs : Aster Richard C., Borchers Brian, Thurber Clifford H.
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.
1. Introduction2. Linear Regression3. Rank Deficiency and Ill-Conditioning4. Tikhonov Regularization5. Discretizing by Basis Functions6. Iterative Methods of Solving Linear Problems7. Additional Regularization Techniques8. Fourier Techniques9. Nonlinear Regression10. Nonlinear Inverse Problems11. Bayesian Methods12 Adjoint Methods
Dr. Borchers’ primary research and teaching interests are in optimization and inverse problems. He teaches a number of undergraduate and graduate courses at NMT in linear programming, nonlinear programming, time series analysis, and geophysical inverse problems. Dr. Borchers’ research has focused on interior point methods for linear and semidefinite programming and applications of these techniques to combinatorial optimization problems. He has also done work on inverse problems in geophysics and hydrology using linear and nonlinear least squares and Tikhonov regularization.
Professor Thurber is an international leader in research on three-dimensional seismic imaging ("seismic tomography") using earthquakes. His primary research interests are in the application of seismic tomography to fault zones, volcanoes, and subduction zones, with a long-term focus on the San Andreas fault in central California and volcanoes in Hawaii and Alaska. Other areas of expertise include earthquake location (the topic of a book he edited) and geophysical inverse theory.
- Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method
- Includes an online instructor’s guide that helps professors teach and customize exercises and select homework problems
- Covers updated information on adjoint methods that are presented in an accessible manner
Date de parution : 10-2018
Ouvrage de 404 p.
19x23.3 cm
Thèmes de Parameter Estimation and Inverse Problems :
Mots-clés :
Assistive technologies; Bayes Theorem; Bayesian Parameter Estimation; Confidence Ellipsoid; Conjugate Gradient Least Squares; Conjugate Gradient Method; Discrete Inverse Problem; Fourier Transform; Fredholm Integral Equation; Gauss�Newton Method; Gradient Descent Method; Human computer interactions; Human machine interface; Inverse Problem; Iterative Resolution Analysis; Kaczmarz's Algorithm; Levenberg�Marquardt Method; Linear Least Squares Problem; Linear Regression; Machine learning; Markov Chain Monte Carlo; Maximum Likelihood Estimation; Monitoring; Monte Carlo Error Propagation; Nonlinear Inverse Problem; Nonlinear Regression; Occam's Inversion; Posterior Distribution; Prior Distribution; Resolution Analysis; Robust Regression; Row Action Method; Seismic Tomography; Simple Collocation; Smart sensor; Stochastic Resolution Analysis; Tikhonov Regularization; User centred design; User involvement; User profile