Solving inverse problems with Bayes' theorem

dc.contributor.author	Latz, Jonas
dc.contributor.author	Sprungk, Björn
dc.contributor.editor	Houston-Edwards, Kelsey
dc.contributor.editor	Singh, Anup Anand
dc.contributor.editor	Randecker, Anja
dc.date.accessioned	2022-09-05T11:49:35Z
dc.date.available	2022-09-05T11:49:35Z
dc.date.issued	2022-09-05
dc.identifier.uri	http://publications.mfo.de/handle/mfo/3972
dc.description.abstract	The 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.iso	en	en_US
dc.publisher	Mathematisches Forschungsinstitut Oberwolfach	en_US
dc.relation.ispartofseries	Snapshots of modern mathematics from Oberwolfach;2022-06
dc.rights	Attribution-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/	*
dc.title	Solving inverse problems with Bayes' theorem	en_US
dc.type	Article	en_US
dc.identifier.doi	10.14760/SNAP-2022-006-EN
local.series.id	SNAP-2022-006-EN	en_US
local.subject.snapshot	Numerics and Scientific Computing	en_US
local.subject.snapshot	Probability Theory and Statistics	en_US
dc.identifier.urn	urn:nbn:de:101:1-2022112209270328668198
dc.identifier.ppn	182315963X

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