We present a review of methods for optimal experimental design (OED) for Bayesian inverse
problems governed by partial differential equations with infinite-dimensional parameters …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Bayesian inverse problems often involve sampling posterior distributions on infinite-
dimensional function spaces. Traditional Markov chain Monte Carlo (MCMC) algorithms are …

Ordinary differential equations (ODEs), via their induced flow maps, provide a powerful
framework to parameterize invertible transformations for representing complex probability …

Traditional approaches to develop 3D geological models employ a mix of quantitative and
qualitative scientific techniques, which do not fully provide quantification of uncertainty in the …

The Bayesian approach to inverse problems provides a rigorous framework for the
incorporation and quantification of uncertainties in measurements, parameters and models …

Abstract Sequential Monte Carlo (SMC) is a reliable method to generate samples from the
posterior parameter distribution in a Bayesian updating context. The method samples a …

This paper considers how to obtain MCMC quantitative convergence bounds which can be
translated into tight complexity bounds in high-dimensional settings. We propose a modified …

Principal feature detection via phi-Sobolev inequalities Page 1 Bernoulli 30(4), 2024, 2979–3003
https://doi.org/10.3150/23-BEJ1702 Principal feature detection via φ-Sobolev inequalities …

A unified performance analysis of likelihood-informed subspace methods Page 1 Bernoulli
28(4), 2022, 2788–2815 https://doi.org/10.3150/21-BEJ1437 A unified performance analysis …