Annealed Importance Sampling

Annealed Importance Sampling (AIS) is a powerful technique for estimating intractable probability distributions by connecting a simple initial distribution to a complex target distribution through a series of intermediate distributions. Current research focuses on improving AIS efficiency and accuracy, particularly within the context of deep generative models like diffusion models and variational autoencoders, often employing techniques like variational inference and normalizing flows to optimize the annealing process and proposal distributions. These advancements enhance the estimation of quantities like marginal likelihoods and mutual information, impacting fields ranging from statistical physics to machine learning by enabling more accurate and efficient inference in complex systems.