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dc.contributor.authorLatz, Jonas
dc.contributor.authorSprungk, Björn
dc.contributor.editorHouston-Edwards, Kelsey
dc.contributor.editorSingh, Anup Anand
dc.contributor.editorRandecker, Anja
dc.date.accessioned2022-09-05T11:49:35Z
dc.date.available2022-09-05T11:49:35Z
dc.date.issued2022-09-05
dc.identifier.urihttp://publications.mfo.de/handle/mfo/3972
dc.description.abstractThe goal of inverse problems is to find an unknown parameter based on noisy data. Such problems appear in a wide range of applications including geophysics, medicine, and chemistry. One method of solving them is known as the Bayesian approach. In this approach, the unknown parameter is modelled as a random variable to reflect its uncertain value. Bayes’ theorem is applied to update our knowledge given new information from noisy data.en_US
dc.language.isoenen_US
dc.publisherMathematisches Forschungsinstitut Oberwolfachen_US
dc.relation.ispartofseriesSnapshots of modern mathematics from Oberwolfach;2022-06
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.titleSolving inverse problems with Bayes' theoremen_US
dc.typeArticleen_US
dc.identifier.doi10.14760/SNAP-2022-006-EN
local.series.idSNAP-2022-006-ENen_US
local.subject.snapshotNumerics and Scientific Computingen_US
local.subject.snapshotProbability Theory and Statisticsen_US
dc.identifier.urnurn:nbn:de:101:1-2022112209270328668198
dc.identifier.ppn182315963X


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Attribution-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-ShareAlike 4.0 International