Statistical inverse problems: Discretization, model reduction and inverse crimes

The article discusses the discretization of linear inverse problems. When an inverse problem is formulated in terms of infinite-dimensional function spaces and then discretized for computational purposes, a discretization error appears. Since inverse problems are typically ill-posed, neglecting this...

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Vydáno v:Journal of computational and applied mathematics Ročník 198; číslo 2; s. 493 - 504
Hlavní autoři: Kaipio, Jari, Somersalo, Erkki
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 15.01.2007
Elsevier
Témata:
ISSN:0377-0427, 1879-1778
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Shrnutí:The article discusses the discretization of linear inverse problems. When an inverse problem is formulated in terms of infinite-dimensional function spaces and then discretized for computational purposes, a discretization error appears. Since inverse problems are typically ill-posed, neglecting this error may have serious consequences to the quality of the reconstruction. The Bayesian paradigm provides tools to estimate the statistics of the discretization error that is made part of the measurement and modelling errors of the estimation problem. This approach also provides tools to reduce the dimensionality of inverse problems in a controlled manner. The ideas are demonstrated with a computed example.
Bibliografie:SourceType-Scholarly Journals-2
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ObjectType-Conference Paper-1
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SourceType-Conference Papers & Proceedings-1
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ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2005.09.027