Startseite Convergence of posteriors for structurally non-identified problems using results from the theory of inverse problems
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Convergence of posteriors for structurally non-identified problems using results from the theory of inverse problems

  • Nicole E. Radde EMAIL logo und Jonas Offtermatt
Veröffentlicht/Copyright: 25. Mai 2013

Abstract.

We consider convergence of the posterior distribution in a Bayesian parameter estimation framework in the large sample size limit for structurally non-identified problems. These belong to the class of ill-posed problems, and the large sample theory is not applicable here. In particular, the influence of the prior distribution does not vanish in the large sample size limit. We review recent results in this area and present ideas inspired from the theory of ill-posed inverse problems that can be used towards a more general concept of posterior convergence for non-identified problems.

MSC: 62F15; 65J20

Funding source: German Research Foundation (DFG)

Award Identifier / Grant number: Cluster of Excellence in Simulation Technology (EXC 310)

Funding source: Ministry of Science, Research and Arts (MWK) Baden-Württemberg

Award Identifier / Grant number: program for junior professors

We thank Barbara Kaltenbacher from the University of Klagenfurt for her careful proof reading of the manuscript and many valid remarks on previous versions of this work.

Received: 2012-8-14
Published Online: 2013-5-25
Published in Print: 2014-4-1

© 2014 by Walter de Gruyter Berlin/Boston

Heruntergeladen am 16.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jip-2012-0057/html
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