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Bibliographic Details
Main Authors:	Lykkegaard, Mikkel B., Fox, Colin, Higdon, Dave, Reese, C. Shane, Moulton, J. David
Format:	Preprint
Published:	2024
Subjects:	Computation
Online Access:	https://arxiv.org/abs/2405.00397
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Table of Contents:

In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various multivariate proposal distributions, and compare them with single-site Metropolis updates. We show how fast, approximate models can be leveraged to improve the MCMC sampling efficiency.