Computational Methods for Inverse Problems

Vogel, Curtis R

ISBN 10: 0898715504 / ISBN 13: 9780898715507

Published by Soc for Industrial & Applied Math, 2002

About this Item

***INTERNATIONAL EDITION*** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments contain tracking numbers. Great professional textbook selling experience and expedite shipping service. Glued binding. 199 p. Audience: General/trade.

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Bibliographic Details

• Title: Computational Methods for Inverse Problems
• Publisher: Soc for Industrial & Applied Math
• Publication Date: 2002
• Language: English
• ISBN 10: 0898715504
• ISBN 13: 9780898715507
• Binding: Trade paperback
• Edition: First edition.
• Condition: New in new dust jacket.

About this title

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.

Book Description

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.