Approximation errors in nonstationary inverse problems

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Title

Approximation errors in nonstationary inverse problems

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

Description

Often, in nonstationary inverse problems, computing estimates with accurate high-dimensional models is not practically feasible. An approach called the Bayesian approximation error (BAE) has been shown to be able to handle highly approximate models. In the BAE approach, the approximation errors (AEs) of the approximate models are modelled through sampling, based on the state evolution model. The approach has previously been implemented for filtering problems. In this thesis, we extend the BAE...

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Format

Research paper

Research format

Thesis

Thesis level

Doctoral

Date created

2019

Creator

Cheon, Eun Sig

URL

https://hdl.handle.net/2292/49365

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