Statistical and Computational Inverse Problems

Author:   Jari Kaipio ,  E. Somersalo
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2005
Volume:   160
ISBN:  

9781441919649


Pages:   340
Publication Date:   29 November 2010
Format:   Paperback
Availability:   In Print   Availability explained
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Statistical and Computational Inverse Problems


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Overview

This book is aimed at postgraduate students in applied mathematics as well as at engineering and physics students with a ?rm background in mathem- ics. The ?rst four chapters can be used as the material for a ?rst course on inverse problems with a focus on computational and statistical aspects. On the other hand, Chapters 3 and 4, which discuss statistical and nonstati- ary inversion methods, can be used by students already having knowldege of classical inversion methods. There is rich literature, including numerous textbooks, on the classical aspects of inverse problems. From the numerical point of view, these books concentrate on problems in which the measurement errors are either very small or in which the error properties are known exactly. In real-world pr- lems, however, the errors are seldom very small and their properties in the deterministic sensearenot wellknown.For example,inclassicalliteraturethe errornorm is usuallyassumed to be a known realnumber. In reality,the error norm is a random variable whose mean might be known.

Full Product Details

Author:   Jari Kaipio ,  E. Somersalo
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of hardcover 1st ed. 2005
Volume:   160
Dimensions:   Width: 15.50cm , Height: 1.80cm , Length: 23.50cm
Weight:   0.545kg
ISBN:  

9781441919649


ISBN 10:   1441919643
Pages:   340
Publication Date:   29 November 2010
Audience:   Professional and scholarly ,  Professional and scholarly ,  Professional & Vocational ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Inverse Problems and Interpretation of Measurements.- Classical Regularization Methods.- Statistical Inversion Theory.- Nonstationary Inverse Problems.- Classical Methods Revisited.- Model Problems.- Case Studies.

Reviews

"From the reviews: ""The book is devoted to the development of the statistical approach to inverse problems ... . The content is written clearly and without citations in the main text. Every chapter has a section called 'Notes and comments, where the citations and further reading, as well as brief comments on more advanced topics, are provided. The book is aimed at postgraduate students ... . The book also will be of interest for many researchers and scientists working in the area of image processing."" (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1068, 2005) ""Inverse problems are usually ill-posed in the sense that a solution need not exist, need not be unique, and depends in a discontinuous way on the data ... . there have been two quite separate communities dealing with such problems, one basing their methods mainly on functional analysis, the other one on statistics. ... several attempts have been made to bridge the gap between these two groups. The book under review ... is a further, quite successful attempt in this direction."" (Heinz W. Engel, SIAM Review, Vol. 48 (1), 2006)"


From the reviews: The book is devoted to the development of the statistical approach to inverse problems ! . The content is written clearly and without citations in the main text. Every chapter has a section called 'Notes and comments' where the citations and further reading, as well as brief comments on more advanced topics, are provided. The book is aimed at postgraduate students ! . The book also will be of interest for many researchers and scientists working in the area of image processing. (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1068, 2005) Inverse problems are usually ill-posed in the sense that a solution need not exist, need not be unique, and depends in a discontinuous way on the data ! . there have been two quite separate communities dealing with such problems, one basing their methods mainly on functional analysis, the other one on statistics. ! several attempts have been made to bridge the gap between these two groups. The book under review ! is a further, quite successful attempt in this direction. (Heinz W. Engel, SIAM Review, Vol. 48 (1), 2006)


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