Target Distribution

Target distribution research focuses on efficiently approximating and sampling from complex probability distributions, crucial for various applications like drug discovery and astronomical simulations. Current efforts center on developing novel generative models, including normalizing flows and diffusion models, often coupled with optimization techniques like annealed importance sampling and gradient flows, to overcome challenges such as representation bias and high dimensionality. These advancements improve the accuracy and efficiency of generating samples from target distributions, impacting fields ranging from machine learning and statistical physics to robotics and healthcare through improved model training and data analysis.