Random forward models and log-likelihoods in Bayesian inverse problems
Zitieren Sie bitte immer diese URN: urn:nbn:de:0297-zib-66324
- We consider the use of randomised forward models and log-likelihoods within the Bayesian approach to inverse problems. Such random approximations to the exact forward model or log-likelihood arise naturally when a computationally expensive model is approximated using a cheaper stochastic surrogate, as in Gaussian process emulation (kriging), or in the field of probabilistic numerical methods. We show that the Hellinger distance between the exact and approximate Bayesian posteriors is bounded by moments of the difference between the true and approximate log-likelihoods. Example applications of these stability results are given for randomised misfit models in large data applications and the probabilistic solution of ordinary differential equations.
Verfasserangaben: | Han Cheng Lie, T. J. Sullivan, Aretha Teckentrup |
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Dokumentart: | ZIB-Report |
Titel des übergeordneten Werkes (Englisch): | SIAM/ASA Journal on Uncertainty Quantification |
Band: | 6 |
Ausgabe / Heft: | 4 |
Erste Seite: | 1600 |
Letzte Seite: | 1629 |
Freies Schlagwort / Tag: | Bayesian inverse problem; posterior consistency; probabilistic numerics; random likelihood; randomised misfit; surrogate model; uncertainty quantification |
MSC-Klassifikation: | 62-XX STATISTICS |
65-XX NUMERICAL ANALYSIS | |
68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area) | |
Datum der Erstveröffentlichung: | 04.01.2018 |
Schriftenreihe (Bandnummer): | ZIB-Report (18-03) |
ArXiv-Id: | http://arxiv.org/abs/1712.05717 |
ISSN: | 1438-0064 |
DOI: | https://doi.org/10.1137/18M1166523 |