7 papers
Detecting Model Misspecification in Bayesian Inverse Problems via Variational Gradient Descent
Qingyang Liu, Matthew A. Fisher, Zheyang Shen +4
Bayesian inference is optimal when the statistical model is well-specified, while outside this setting Bayesian inference can catastrophically fail; accordingly a wealth of post-Ba…
Response to: "A note on conditional densities, Bayes' rule, and recent criticisms of Bayesian inference" by Yan et al., 2026
Klaus Mosegaard, Andrew Curtis
In a recent preprint (Mosegaard and Curtis, 2024, arXiv:2411.13570v2) we analyzed the consequences of ignoring the well-known inconsistency of classical conditional probability den…
Designing Solutions to Geophysical Inverse Problems by Changing Variables
Xuebin Zhao, Andrew Curtis, Klaus Mosegaard
Geoscientists often solve inverse problems to estimate values of parameters of interest given relevant data sets. Bayesian inference solves these problems by combining probability…
Linearised versus Nonlinear Estimates of Uncertainty in Full Waveform Inversion
Xuebin Zhao, Andrew Curtis
Seismic full waveform inversion (FWI) is a powerful technique to generate high resolution images of the Earth's interior. However, significant uncertainty exists in all FWI solutio…
Variational and Monte Carlo Methods for Bayesian Inversion of Dynamic Subsurface Flow Simulations Using Seismic and Fluid Pressure Data
Zhen Zhang, Xuebin Zhao, Andrew Curtis
In order to predict future performance of subsurface fluid reservoirs under possible operating scenarios, a dynamic, porous-medium flow simulation model must be tuned to include re…
Direct-3D Variational Bayesian Surface Wave Inversion and Its Application to Ambient Noise Tomography beneath Great Britain
Xuebin Zhao, Lily Irvin, Erica Galetti +1
We present a new, variational, fully nonlinear, probabilistic ambient noise tomography method, which estimates subsurface structure and quantifies the corresponding uncertainties d…