Computational Methods for Inverse Problems

Curtis R Vogel

ISBN 10: 0898715504 / ISBN 13: 9780898715507

Edité par Society For Industrial And Applied Mathematics (SIAM) Jan 1987, 1987

A propos de cet article

Neuware - Inverse problems arise in a number of important practical applications, ranging from biomedical imaging to seismic prospecting. This book provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. It also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods. Because inverse problems typically involve the estimation of certain quantities based on indirect measurements, the estimation process is often ill-posed. Regularization methods, which have been developed to deal with this ill-posedness, are carefully explained in the early chapters of Computational Methods for Inverse Problems. The book also integrates mathematical and statistical theory with applications and practical computational methods, including topics like maximum likelihood estimation and Bayesian estimation.

N° de réf. du vendeur 9780898715507

Détails bibliographiques

• Titre: Computational Methods for Inverse Problems
• Éditeur: Society For Industrial And Applied Mathematics (SIAM) Jan 1987
• Date de parution: 1987
• Langue: anglais
• ISBN à 10 chiffres: 0898715504
• ISBN à 13 chiffres: 9780898715507
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

Inverse problems arise in a number of important practical applications, ranging from biomedical imaging to seismic prospecting. This book provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. It also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods. Because inverse problems typically involve the estimation of certain quantities based on indirect measurements, the estimation process is often ill-posed. Regularization methods, which have been developed to deal with this ill-posedness, are carefully explained in the early chapters of Computational Methods for Inverse Problems. The book also integrates mathematical and statistical theory with applications and practical computational methods, including topics like maximum likelihood estimation and Bayesian estimation.