Posterior Sampling
Posterior sampling aims to efficiently generate samples from a probability distribution representing the posterior belief about unknown parameters given observed data. Current research focuses on improving the efficiency and accuracy of posterior sampling, particularly within the context of high-dimensional data and complex models, employing techniques like diffusion models, normalizing flows, and Langevin dynamics. These advancements are impacting diverse fields, including image processing, Bayesian inverse problems, and reinforcement learning, by enabling more robust and efficient inference in challenging scenarios. The development of computationally tractable algorithms for posterior sampling is crucial for advancing these areas.
Papers
October 29, 2024
October 27, 2024
October 21, 2024
October 16, 2024
October 7, 2024
October 4, 2024
October 3, 2024
October 2, 2024
September 12, 2024
July 26, 2024
July 25, 2024
July 24, 2024
July 3, 2024
July 1, 2024
June 30, 2024
June 18, 2024
June 4, 2024
May 29, 2024