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Posterior consistency for Bayesian inverse problems through stability and regression results

Abstract:

In the Bayesian approach, the a priori knowledge about the input of a mathematical model is described via a probability measure. The joint distribution of the unknown input and the data is then conditioned, using Bayes' formula, giving rise to the posterior distribution on the unknown input. In this setting we prove posterior consistency for nonlinear inverse problems: a sequence of data is considered, with diminishing fluctuations around a single truth and it is then of interest to show that...

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Authors


Vollmer, SJ More by this author
Journal:
Inverse Problems
Volume:
29
Issue:
12
Pages:
125011-125011
Publication date:
2013-12-05
DOI:
EISSN:
1361-6420
ISSN:
0266-5611
URN:
uuid:f4fe858c-7696-4a9a-8a78-ea4aa53a08bb
Source identifiers:
487901
Local pid:
pubs:487901
Language:
English

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