Error Bounds for Some Approximate Posterior Measures in Bayesian Inference

Han Cheng Lie*, T. J. Sullivan, Aretha Teckentrup

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In certain applications involving the solution of a Bayesian inverse problem, it may not be possible or desirable to evaluate the full posterior, e.g. due to the high computational cost of doing so. This problem motivates the use of approximate posteriors that arise from approximating the data misfit or forward model. We review some error bounds for random and deterministic approximate posteriors that arise when the approximate data misfits and approximate forward models are random.

Original languageEnglish
Title of host publicationNumerical Mathematics and Advanced Applications, ENUMATH 2019 - European Conference
EditorsFred J. Vermolen, Cornelis Vuik
PublisherSpringer Science and Business Media Deutschland GmbH
Pages275-283
Number of pages9
ISBN (Print)9783030558734
DOIs
Publication statusPublished - 2021
EventEuropean Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2019 - Egmond aan Zee, Netherlands
Duration: 30 Sep 20194 Oct 2019

Publication series

NameLecture Notes in Computational Science and Engineering
Volume139
ISSN (Print)1439-7358
ISSN (Electronic)2197-7100

Conference

ConferenceEuropean Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2019
CountryNetherlands
CityEgmond aan Zee
Period30/09/194/10/19

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