Unnormalized Density

Unnormalized density sampling focuses on efficiently generating samples from probability distributions whose normalization constant is unknown or computationally intractable, a common challenge in various fields. Current research emphasizes developing novel algorithms, including those based on annealed importance sampling, normalizing flows, Langevin dynamics, and stochastic optimal control, often incorporating neural networks to learn optimal sampling strategies. These advancements improve the accuracy and efficiency of sampling from complex, high-dimensional distributions, with applications ranging from Bayesian inference and generative modeling to statistical physics and machine learning. The development of robust and efficient samplers for unnormalized densities is crucial for advancing these fields.