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Bayesian Approach to Inverse Problems
Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-des...
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Lenguaje: | eng |
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Wiley
2008
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Acceso en línea: | http://cds.cern.ch/record/1123120 |
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author | Idier, Jerome |
author_facet | Idier, Jerome |
author_sort | Idier, Jerome |
collection | CERN |
description | Many scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems.The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimation |
id | cern-1123120 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2008 |
publisher | Wiley |
record_format | invenio |
spelling | cern-11231202021-04-22T01:45:32Zhttp://cds.cern.ch/record/1123120engIdier, JeromeBayesian Approach to Inverse ProblemsMathematical Physics and MathematicsMany scientific, medical or engineering problems raise the issue of recovering some physical quantities from indirect measurements; for instance, detecting or quantifying flaws or cracks within a material from acoustic or electromagnetic measurements at its surface is an essential problem of non-destructive evaluation. The concept of inverse problems precisely originates from the idea of inverting the laws of physics to recover a quantity of interest from measurable data.Unfortunately, most inverse problems are ill-posed, which means that precise and stable solutions are not easy to devise. Regularization is the key concept to solve inverse problems.The goal of this book is to deal with inverse problems and regularized solutions using the Bayesian statistical tools, with a particular view to signal and image estimationWileyoai:cds.cern.ch:11231202008 |
spellingShingle | Mathematical Physics and Mathematics Idier, Jerome Bayesian Approach to Inverse Problems |
title | Bayesian Approach to Inverse Problems |
title_full | Bayesian Approach to Inverse Problems |
title_fullStr | Bayesian Approach to Inverse Problems |
title_full_unstemmed | Bayesian Approach to Inverse Problems |
title_short | Bayesian Approach to Inverse Problems |
title_sort | bayesian approach to inverse problems |
topic | Mathematical Physics and Mathematics |
url | http://cds.cern.ch/record/1123120 |
work_keys_str_mv | AT idierjerome bayesianapproachtoinverseproblems |