4 papers
A Majorization-Minimization with Monte Carlo Approach for Hyperparameter Estimation
Elle Buser, Julianne Chung, Hugo DÃaz +1
We consider inverse problems with linear forward models and Gaussian priors, but with unknown hyperparameters that may arise from the model, the noise, or the specification of the…
Efficient sampling approaches based on generalized Golub-Kahan methods for large-scale hierarchical Bayesian inverse problems
Elle Buser, Julianne Chung
Uncertainty quantification for large-scale inverse problems remains a challenging task. For linear inverse problems with additive Gaussian noise and Gaussian priors, the posterior…
Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation
Julianne Chung, Scot M. Miller, Malena Sabate Landman +1
In Bayesian inverse problems, it is common to consider several hyperparameters that define the prior and the noise model that must be estimated from the data. In particular, we are…
Inexact Generalized Golub-Kahan Methods for Large-Scale Bayesian Inverse Problems
Yutong Bu, Julianne Chung
Solving large-scale Bayesian inverse problems presents significant challenges, particularly when the exact (discretized) forward operator is unavailable. These challenges often ari…