Data Driven Prior

Data-driven priors leverage machine learning to incorporate prior knowledge into Bayesian inference, improving the accuracy and efficiency of various inverse problems. Current research focuses on developing and refining data-driven priors using deep generative models (like score-based models and neural processes), and integrating them into diverse algorithms such as Bayesian optimization and Markov Chain Monte Carlo methods. This approach enhances the robustness and generalizability of inference across various fields, including imaging, astrophysics, and robotics, by effectively encoding complex, high-dimensional information learned from data.