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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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Autor principal: Idier, Jerome
Lenguaje:eng
Publicado: Wiley 2008
Materias:
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
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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