Deep Learning Enhances Bayesian Inverse Problems with Hierarchical MCMC Sampling

This research article presents a novel approach to Bayesian inverse problems by integrating deep neural networks with hierarchical MCMC sampling. The methodology shows promise in handling complex problems by combining multiple solvers and leveraging the strengths of deep learning.

• •Applies hierarchical MCMC sampling for Bayesian inverse problems.
• •Integrates multiple solvers using deep neural networks.
• •Published on ArXiv, suggesting early-stage research.